GABAergic regulation of action-outcome priors in conditions associated with frontotemporal degeneration

GABAergic regulation of action-outcome priors in conditions associated with frontotemporal degeneration | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search GABAergic regulation of action-outcome priors in conditions associated with frontotemporal degeneration View ORCID Profile Rebecca S. Williams , Michelle Naessens , Emily G Todd , Robert Durcan , David J Whiteside , Juliette Lanskey , Amirhossein Jafarian , View ORCID Profile Karl Friston , View ORCID Profile Laura E. Hughes , View ORCID Profile James B. Rowe doi: https://doi.org/10.1101/2025.10.22.25338552 Rebecca S. Williams 1 Medical Research Council Cognition and Brain Sciences Unit , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rebecca S. Williams For correspondence: rebecca.williams{at}mrc-cbu.cam.ac.uk Michelle Naessens 1 Medical Research Council Cognition and Brain Sciences Unit , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily G Todd 1 Medical Research Council Cognition and Brain Sciences Unit , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robert Durcan 2 Department of Clinical Neurosciences, University of Cambridge , UK 3 Cambridge University Hospitals NHS Trust , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site David J Whiteside 2 Department of Clinical Neurosciences, University of Cambridge , UK 3 Cambridge University Hospitals NHS Trust , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juliette Lanskey 1 Medical Research Council Cognition and Brain Sciences Unit , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amirhossein Jafarian 1 Medical Research Council Cognition and Brain Sciences Unit , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karl Friston 4 The Wellcome Centre for Human Neuroimaging, University College London , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karl Friston Laura E. Hughes 1 Medical Research Council Cognition and Brain Sciences Unit , Cambridge, UK 2 Department of Clinical Neurosciences, University of Cambridge , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura E. Hughes James B. Rowe 1 Medical Research Council Cognition and Brain Sciences Unit , Cambridge, UK 2 Department of Clinical Neurosciences, University of Cambridge , UK 3 Cambridge University Hospitals NHS Trust , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James B. Rowe Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Rationale Apathy is a common symptom in many neurological and psychiatric conditions, associated with poor prognosis and increased caregiver burden. There are currently no proven treatments, in part due to a lack of mechanistic understanding. We have proposed a novel framework for apathy, based on the failure of active inference due to imprecise priors on the outcome of actions. The loss of precision on action outcomes causes apathy by reducing the expected difference between the state of the world following action versus non-action. Here we test the hypothesis that the loss of prior precision on action outcomes is reversible and mediated neuronally by GABAergic gain on the superficial pyramidal neurons in a prefrontal-motor decision-making hierarchy. We test this in a healthy cohort and people with two syndromes associated with frontotemporal lobar degeneration. Methods Twenty healthy controls, twenty people with behavioural variant frontotemporal dementia (bvFTD) and twenty people with progressive supranuclear palsy (PSP) took part in a randomised placebo-controlled double-blind trial using zolpidem, an established GABAA agonist. This study was registered with ISRCTN registry ( ISRCTN10616794 ). We use the ‘Goal Prior Assay’ task and dynamic causal modelling of MEG resting-state data to explore the cognitive and neural concomitants of prior precision, and the effect of GABAergic regulation on both prior precision and superficial pyramidal gain. Apathy was primarily assessed with the Apathy Evaluation Scale (Self and Carer). Principal analyses were conducted using Bayesian statistics, supplemented by classical frequentist tests. Results Forty-three participants (20 controls and 23 patients) were included in the final analysis. We found strong evidence for a difference in measures of apathy (B>1000, p<0.001) and prior precision (B=20.4, p<0.01) between healthy controls and people with bvFTD and PSP. This difference in prior precision was not found in the drug condition (B=0.86, p=0.11). There was strong evidence of a correlation between apathy and prior precision across groups (B>100, p<0.001). Dynamic causal modelling of MEG resting-state data confirmed reductions in gain on the prefrontal superficial pyramidal neurons in patients. The prefrontal superficial pyramidal gain was partially restored on zolpidem and linked to participants’ prior precision on their action outcomes. Conclusion We confirm that apathy in conditions associated with frontotemporal lobar degeneration is underwritten by a reduction in prior precision on action outcomes, mediated by reduced synaptic gain of prefrontal superficial pyramidal neurons. GABAergic regulation using zolpidem partially restores prior precision by acting on this gain and reinstating neuronal message-passing within the prefrontal-motor decision-making hierarchy. Introduction Apathy is a common symptom in many neurological and psychiatric disorders, including syndromes associated with frontotemporal lobar degeneration 1 (FTLD). It is defined as a reduction in goal-directed action 2 , and predicts both functional independence 3 and mortality 4 over and above age or cognitive ability. Apathy is positively associated with caregiver burden 5 , 6 and is a priority target symptom for treatment in dementia by both clinicians and carers 7 , 8 . While preclinical models of apathy have emphasised the role of reward and dopaminergic depletion, apathy can also be considered as the result of a failure of active inference, resulting from the loss of prior precision on action outcomes 9 . In the ‘Bayesian Brain 10 – 12 , prior beliefs are key to learning accurate models of the environment, including action outcomes. Priors are combined with sensory evidence to update beliefs about the world, allowing us to predict and interact with our surroundings. When there is a discrepancy between predicted outcomes and sensory observations, beliefs may be updated passively (i.e., changing one’s perception or understanding of the situation), or one may actively shape the environment, so that it better matches with prior expectations (active inference) 13 . Prior precision, characterised as the certainty or confidence afforded a prior belief, is a key factor in active inference. When prior precision on action outcomes is very low, the difference between the expected consequences of acting versus not-acting disappears. For example, if your beliefs about how much warmer you will be with a jacket on are imprecise, you are less likely to take the action of putting a jacket on to keep warm. This prior precision is a distinct property from the magnitude of the expected reward. Apathy can therefore arise as a failure of active inference due to imprecise priors on action outcomes, as there is an implicit uncertainty about how actions will bring the world closer to a goal state. This hypothesis is supported by the association between apathy and prior precision in healthy individuals and in people with Parkinson’s disease 9 , 14 , 15 . There are currently no proven pharmacological treatments for apathy 16 . Selective serotonin reuptake inhibitors are commonly used to treat apathy in FTLD 8 despite mixed evidence of efficacy 17 – 19 and concerns of worsening amotivational syndromes 20 . Similarly, selective dopamine agonists and levodopa in isolation do not reliably treat clinical apathy 16 , 21 – 23 . However, compounds with joint noradrenergic and dopaminergic action like methylphenidate have shown some positive effects 24 – 26 , including in neurological conditions such as Alzheimer’s disease 24 . Noradrenaline has also been linked to the related processes of effort processing 27 , learning under uncertainty 28 , and prior precision 29 . Such noradrenergic effects on prior precision may be direct, or indirect, through interactions with gamma-aminobutyric acid (GABA) 30 – 34 . GABAergic deficits are well-established in FTLD-associated conditions, with evidence of reduced pyramidal neurons 35 and GABA-receptors 36 , 37 in the frontal and temporal lobes. A GABA deficit has also been confirmed in vivo in behavioural variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy (PSP) using magnetic resonance spectroscopy 38 . The consequence of these GABA deficits has been further characterised using dynamic causal models of human cortical physiology 39 . GABA is critical in maintaining the dynamic modulation of information processing in the frontal cortex 40 , and its loss results in effective disconnection in networks for goal-directed decision-making. In these networks, modulation of synaptic gain on superficial pyramidal cells by GABA is proposed to encode or mediate prior precision 40 . If confirmed, this hypothesis would open new GABAergic strategies to normalise prior precision, and in turn treat apathy. We test this hypothesis using zolpidem, a GABA A receptor agonist (licensed as a sedative-hypnotic). Zolpidem modulates GABA levels and glucose metabolism in human prefrontal cortex 41 and has been used off-label for the treatment of symptoms in disorders of movement 42 and consciousness 43 , with small scale studies of severe amotivational states reporting benefits 41 , 44 , 45 . Preclinical studies also demonstrate potential anti-apathy mechanisms 46 , with the potential for broader clinical impacts highlighted in conditions like PSP 47 . We therefore selected zolpidem to test the hypothesis that GABAergic modulation may improve prior precision in people at risk of a GABAergic deficit as a result of bvFTD or PSP. We undertook a randomised placebo-controlled double-blind trial to test the neural and computational mechanisms underpinning apathy. Specifically, we hypothesised that zolpidem would improve prior precision in individuals with bvFTD and PSP, but not in healthy controls, by restoring GABAergic neuromodulatory mechanisms in the prefrontal cortex. We investigate these neural mechanisms using dynamic causal modelling of resting-state MEG data, with a focus on the gain of superficial pyramidal neurons in the prefrontal cortex. This mechanistic study is not a Clinical Trial of an Investigational Medicinal Product (i.e. not a CTIMP), but confirmation of the hypothesis would indicate the potential for future CTIMPS to evaluate GABAergic drugs to reduce apathy. Materials and methods Participants Twenty people with probable behavioural variant frontotemporal dementia (bvFTD 48 ), 20 people with probable progressive supranuclear palsy (PSP - Richardson syndrome 49 ) and 20 healthy controls were recruited. Recruitment for this stage of the study took place from March 2022 to January 2025. The controls were recruited from either the volunteer panel of the MRC Cognition and Brain Sciences Unit or the Join Dementia Research register. Healthy volunteers were included if they were right-handed, aged between 50 and 75 years old, and had normal or corrected-to-normal vision and hearing. Volunteers with probable bvFTD or PSP were recruited from the NHS dementia clinics. Exclusion criteria in both groups included those currently taking GABAergic medications, and individuals with a history of brain injuries. Normal clinical care continued through participation in the study with no change in concomitant medications. Diagnoses were made by a consultant neurologist at a multidisciplinary clinic, based on international consensus criterion. Individuals with other types of dementia, or PSP phenotypes, were not included. The study was registered with ISRCTN registry ( ISRCTN10616794 ; https://www.isrctn.com/ISRCTN10616794 ) and the protocol was exempted from clinical trials status by the Medicines and Healthcare Products Regulatory Agency, UK, as a mechanistic study with non-clinical primary outcomes. Participants gave written informed consent according to the Declaration of Helsinki, and the study protocol was approved by the local Research Ethics Committee. The duration of the study days, disease severity, behavioural and technical factors meant that approximately half of patients were able to complete the task with sufficient data and quality for analysis. There are clinical and neurological distinctions between bvFTD, and PSP. However, there is also significant clinical overlap between the two, including apathy, prefrontal neurophysiological deficits and GABAergic deficits 39 . We therefore collapsed across diagnostic groups for principal analyses and separated them for secondary tests. Procedure Participants completed three testing sessions. The first two sessions constituted a randomized double-blind placebo-controlled crossover design and were conducted approximately two weeks apart, at the same time of day. Participants were given a single dose 5mg oral tablet of zolpidem or matched placebo. The randomisation order was permuted in sequential blocks of six participants and known only to the dispensing pharmacist. The Clinical Research Facility team (independent of the study team) administered the tablets on each visit at Addenbrookes Hospital, Cambridge, UK. Venus blood samples were taken approximately 90 minutes after drug administration, immediately before the MEG recording, close in time to the estimated peak plasma levels and central nervous system penetration. MEG recordings took place at the MRC Cognition & Brain Sciences Unit, University of Cambridge, UK. At the start of each session, participants completed a 5-minute resting-state MEG recording with eyes closed. At the end of the session, participants completed the ‘Goal Prior Assay’ paradigm 14 (detailed below) out-of-scanner. The third testing session included 7-Tesla MRI — for source reconstruction of the MEG data — at the Wolfson Brain Imaging Centre, University of Cambridge, UK. Materials The primary behavioural task in this study was the ‘Goal Prior Assay’ task, developed by Hezemans et al. (2020) 14 . This task was designed to infer the precision of prior beliefs and its influence on the perception of action outcomes. The aim of the task is for participants to land a virtual ball on a target by applying sufficient power to a force sensor. In a pseudo-random subset of trials the ball disappears before reaching its end position, and participants are asked to estimate where they believe the ball’s final position to be (see figure 1 ). Download figure Open in new tab Figure 1 The ‘Goal Prior Assay’ task originated by Hezemans et al (2020). When the ball appears, participants are asked to press a force sensor with sufficient force, sustained for 3s, for it to reach the target under learned stable kinematics. On pressing the sensor, the ball turns red. After 3 seconds, the ball turns green, and participants release the force sensor. The virtual ball then travels across the screen, with initial velocity scaled to the average force applied to the sensor, subject to constant deceleration. In non-catch trials, the ball is seen to stop. In pseudo-random catch trials, the ball disappears early in its trajectory and participants are asked to estimate the final position of the ball using an estimation scale that appears on screen. The task consisted of 120 trials, including 40 estimation trials, with 15 additional practise trials, which are excluded from analysis. The trials are split into 4 blocks, randomised in their presentation. 2 blocks are considered ‘high effort’ (i.e. the target is farther away, requiring high force) and 2 blocks ‘low effort’ (i.e. the target is closer, requiring low force). MEG Data Acquisition MEG data were acquired at 1 kHz in a magnetically shielded room using the 306-channel MEGIN TRIUX neuro system which includes 204 planar gradiometers and 102 magnetometers. Eye movements were measured using pairs of vertical and horizontal EOG electrodes. Prior to data acquisition, a 3D digitizer (Fastrak Polyhemis Inc.) was used to record (1) three fiducial points, one on the nasion and one each on the left/right pre-auricular points, (2) the location of five HPI coils, and (3) ∼300 ‘head points’ across the scalp. The preprocessing pipeline for our resting state data is based on Vaghari et al (2022; https://github.com/delshadv/BioFIND-data-paper ). MEG preprocessing was conducted in MATLAB v2018a using the Statistical Parametric Mapping (SPM12) toolbox. Raw MEG data were preprocessed using Maxfilter software (version 2.2.14, Neuro-TRIUX), including standard signal-space separation, movement compensation and translation to default space. The data were segmented into epochs of 1 second duration and downsampled to 500Hz, and filtered using a 0.5-150Hz bandpass filter followed by 48-52Hz notch filter. Artefact rejection used the OHBA Software Library. Finally, the cleaned data was manually coregistered to a 7T scan (N=28) using the fiducials digitised at MEG acquisition. Where a 7T MRI scan was not available, a volumetric 3T MRI scan was instead used for coregistration (N=10). Where neither was available, we used a symmetrical 7T template ( https://www.templateflow.org/ 51 ). One participant was unable to tolerate either an MEG or MRI scan. 7T MRI used a Siemens MAGNETOM Terra scanner with a 32-channel head receiver and single channel transmit head coil (Nova Medical) at the Wolfson Brain Imaging Centre, University of Cambridge. A high resolution isotropic whole brain Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) sequence (repetition time=4300 ms, echo time=1.99 ms, resolution=99 ms, bandwidth=250 Hz/px, voxel size=0.75 mm3, field of view= 240 × 240 × 157mm, acceleration factor (A ≫ P) =3, flip-angle=5/6° and inversion times=840/2370 ms) was acquired for the purposes of MEG coregistration. 3T MRI used a Siemens PRISMA scanner, with a 32-channel head receiver and single channel transmit head coil (Nova Medical) with T1-weighted Magnetization Prepared Rapid Acquisition Gradient Echoes (MPRAGE; TR=2000ms, TE=2.93ms, TI=850ms, FA=8°, 208 slices, 1.1mm isotropic voxels). Modelling and Statistical Analysis Principal analyses were conducted using Bayesian statistics. For completeness, frequentist statistics are also reported but note that they are not interchangeable tests as the nature of the inferences supported is different. The Bayes Factor (B) is interpreted in keeping with convention: standard evidentiary thresholds are used for moderate (>3), strong (>10) or very strong (>100) 52 , 53 evidence in favour of the alternate hypothesis, or 1/3, 1/10 and 1/100 for corresponding strength of evidence for the null hypothesis. In the case of frequentist statistics, results are considered significant if p<0.05. Participants with relevant data missing were excluded at the level of individual analyses. Analyses comparing the demographic and clinical characteristics of responders and non-responders to the ‘Goal Prior Assay Task’ are reported in Supplementary Material 1. Code for this data analysis pipeline is available at https://github.com/BeccaSue99/prior-GABA . Behavioural Behavioural analyses were conducted with custom scripts using R (v4.3.1) with RStudio (2023). Our primary hypothesis is that individuals with bvFTD and PSP are (a) more apathetic and (b) have more imprecise priors relative to healthy controls. This was analysed using two Bayesian pseudo-t-tests, comparing apathy scores and prior precision between patients and controls. Apathy was measured using the Apathy Evaluation Scale (AES; Self and Carer) 54 and the motivation subscale of the Cambridge Questionnaire for Apathy and Impulsivity Traits (CamQUAIT-M) 55 . Prior precision in this task was operationalised as the best-fit regression slope between performance and estimation error on the ‘Goal Prior Assay’ task. Performance error was the difference between the target and final ball position, whereas estimation error was the difference between the estimate and final ball position. Participants with greater prior precision should be biased towards the location of the target (i.e. their action outcome prior), meaning a negative association between performance and estimation error. The gradient of the regression slope can therefore be used as a proxy of prior precision. We also hypothesised that (a) individuals with FTLD-associated conditions on zolpidem would have more precise priors than those on placebo, but (b) this would not be the case for healthy controls. Given the established GABA deficits in FTLD 56 , we hypothesise that zolpidem may succeed in increasing GABA levels and improve prior precision through augmenting cortical gain on the prefrontal superficial pyramidal neurons. However, as healthy controls have normal GABA levels, we hypothesised that zolpidem may lead to over-dosing of GABA resulting in no improvement, and potentially worsening of, prior precision. We tested these hypotheses using Bayesian pseudo t-tests. We analyse the significance of drug-group interaction by comparing evidence for linear regression models with and without an interaction term. Our co-primary hypothesis is that there is a positive correlation between prior precision and apathy. This is a replication of previous work by Hezemans et al. (2020), implemented in a new diagnostic group and assessed using a Bayesian correlation. Dynamic causal Modelling We implemented dynamic causal modelling (DCM) of cross spectral densities to analyse resting-state data of participants in MEG. DCM is a generative modelling procedure designed to characterise the synaptic connectivity of neuronal networks using neural mass models and Bayesian modelling procedures (i.e., Variational Laplace) 57 – 59 . DCM operates by identifying a biologically-plausible neuronal model which is inverted and fitted to the dataset by varying key parameters, such as intrinsic connectivity between neuronal populations within a cortical source, and extrinsic connectivity between sources. We predicted a reduction in gain of the superficial pyramidal neurons in the prefrontal cortex and a reduction in the top-down extrinsic connectivity needed for integration of prior beliefs with sensory evidence. Based on prior literature 60 and previous modelling of the ‘Goal Prior Assay’ task in scanner 9 we selected three sources of interest in our model: right inferior frontal gyrus (pars orbitalis) [56; 26; -12], left premotor cortex [-14; 16; 58], and left motor cortex [-37; -25; 64]. We investigated the role of diagnostic group, drug condition and prior precision in modulating estimates of connectivity within and between these three sources using Parametric Empirical Bayes (PEB) 61 . Results To ensure effective estimates of prior precision, participants were included in analysis if they completed two or more of the four testing blocks (with catch trials > 20). This led to the inclusion of 20 controls and 17 patients in the placebo condition, and 19 controls and 21 patients in the zolpidem condition. Differences in demographics and clinical scores between patient responders and non-responders is available in Supplementary Material 1. 19 controls and 15 patients had complete data from both visits. There were no unexpected adverse reactions. Demographics of the participants with complete data from one or both sessions is available in Table 1 . There was no evidence of a difference in age between controls and patients ( B=0.36, p=0.53 ), though there was strong evidence for a difference in years of education ( B=29.3, p<0.01 ) with patients having less education than controls. There was also very strong evidence of a difference in ACE-R and CBI-R between controls and patients, with patients being significantly more impaired ( B>100, p<0.001 ). View this table: View inline View popup Download powerpoint Table 1 Demographic and diagnostic data for participants with usable task data in one or both conditions. Values presented are the mean and standard deviation of available data for each metric with comparisons analysed using Bayesian and frequentist t-tests, except gender differences which was analysed using Bayesian contingency tables and a chi-squared test. Behavioural There was very strong evidence of a difference in apathy scores between controls and patients for the AES-Self ( B>1000, p1000, p1000, p<0.001, d=2.26 ) such that patients scored significantly higher than controls. The median prior precision in controls was 0.49 in the placebo condition and 0.53 in the drug condition. The median prior precision in patients was 0.12 in the placebo condition and 0.32 in the drug condition (see figure 2 ). There was no evidence for a difference in prior precision between patients with a diagnosis of bvFTD or PSP ( B=0.45, p=0.64, d=0.21 ), and so groups remained combined for subsequent principal analyses. Download figure Open in new tab Figure 2 Prior precision in controls and patients with FTLD-associated disorders, in placebo and zolpidem conditions. Higher values indicate higher levels of prior precision. The critical result is that there was evidence for an improvement in prior precision in the patient group when on zolpidem vs placebo ( B=3.09, p=0.04, d=0.48 ), and evidence for no difference in prior precision between placebo and drug condition in controls ( B=0.25, p=0.86, d=0.07 ). There was strong evidence for a difference between controls and patients in the placebo condition ( B=20.4, p<0.01, d=1.12 ) such that controls had higher levels of prior precision than patients, but no evidence for or against a difference between controls and patients in the drug condition ( B=0.86, p=0.11, d=0.5 ). There was no evidence for or against an interaction between group and condition ( B=1.31 ). The second most important result was that across all participants (patients and controls), there was very strong evidence for a correlation between prior precision and apathy as assessed by AES-Self ( r=-0.46, B>100, p<0.001 ), as well as moderate evidence for a correlation between prior precision and AES-Carer ( r=-0.33, B=5.82, p=0.01 ) and CamQUAIT-M ( r=-0.29, B=3.07, p=0.02 ; see figure 3 ). When separating by diagnostic groups, there remained moderate evidence for a positive correlation between prior precision and the AES-Self in controls (r=-0.37, B=3.44, p=0.02 ), but no evidence for or against a correlation when using the AES-Carer ( r=0.18, B=0.59, p=0.36 ) and CamQUAIT-M ( r=0.23, B=0.73, p=0.25 ). There was no evidence for or against a correlation between prior precision and apathy in patients (AES-Self: r=-0.3, B=1.61, p=0.06 ; AES-Carer: r=-0.26, B=0.91, p=0.16 ; CamQUAIT-M: r=-0.23, B=0.77, p=0.21 ). Download figure Open in new tab Figure 3 The correlation between apathy and prior precision as assessed by (A) the AES-Self, (B) AES-Carer, and (C) CamQUAIT-M. Reported statistics are for the combined group of controls and patients. When collapsing across group and condition, there was no evidence for or against a correlation between age or education and prior precision (Age: r=-0.16, B=0.6, p=0.18, Education: r=-0.24, B=2.1, p=0.03 ). With separate groups, there was no evidence for or against a correlation between prior precision and age or education in patients (Age: r=-0.15, B=0.52, p=0.36 , Education: r=0.008, B=0.36, p=0.96 ) or controls (Age: r=-0.09, B=0.42, p=0.58 , Education: r=0.2, B=0.65, p=0.23 ). Partial correlations were run to explore the impact of including age and education on the relationship between apathy and prior precision but there were no significant effects. There was no evidence for a correlation between the AES-Self and AES-Carer in either controls ( r=0.16, B=0.54, p=0.4 ) or patients ( r=0.17, B=0.58, p=0.47 ). There was very strong evidence for a correlation between the AES-Carer and CamQUAIT-M in both groups ( r=0.93, B>1000, p<0.001 for both groups ). Dynamic Causal Modelling We compared extrinsic and intrinsic connectivity between controls and individuals with FTLD-associated disorders in both drug and placebo conditions (see figure 4 ). In the placebo condition, there was strong evidence of a reduction in extrinsic connectivity between the premotor and prefrontal cortex in patients compared to controls. In the drug condition, there was evidence that the reciprocal connection from the prefrontal to premotor cortex was increased in individuals with FTLD. In both conditions forward connectivity from the motor to premotor and prefrontal cortex was increased in FTLD versus controls while backward connectivity from the motor to premotor and prefrontal cortex was reduced. Download figure Open in new tab Figure 4 The difference in (A) extrinsic connectivity and (B) superficial pyramidal gain in the placebo and drug conditions. On the y axis, 0 is the mean across patients and controls, positive values indicate parameters that are higher in the patient group than controls, and negative values parameters that are higher in controls than patients. Regarding the difference in gain on the prefrontal superficial pyramidal neurons, there was strong evidence that gain was reduced in patients compared to controls in the placebo condition but not in the zolpidem condition. Gain on the superficial pyramidal neurons in the premotor and motor cortex was reduced in patients compared to controls in the placebo condition but was not different from controls in the zolpidem condition. The second model compared drug conditions for controls and patients. There was no evidence of a difference in extrinsic connectivity between drug conditions for controls, but strong evidence of an increase in the connectivity from the premotor to prefrontal cortex in patients on zolpidem compared to placebo (Pp>0.99) . In controls, there was strong evidence for a decrease in gain on the superficial pyramidal neurons following GABAergic modulation using zolpidem across all three brain regions. However, there was evidence of an increase in gain in the prefrontal cortex in individuals with FTLD-associated disorders. Models using only parameters reflecting GABA time constants had higher model evidence than those incorporating only AMPA or NMDA time constants when explaining the difference between conditions in both patients ( ∇AMPA=466.09, ∇NMDA=378.89 ) and controls ( ∇AMPA=197.15, ∇NMDA=160.16 ; see figure 5 ). However, GABA time constants were not sensitive to drug effects. Download figure Open in new tab Figure 5 A comparison of free energy for models featuring only GABA, AMPA or NMDA time constants. These are models of the difference between drug and placebo in (A) patients with FTLD and (B) controls The third set of (PEB) models incorporated individuals’ prior precision as an empirical prior. In the placebo condition, lower prior precision in those with FTLD-associated conditions was associated with higher reciprocal connectivity between the prefrontal and premotor cortex, however this association altered in the drug condition such that lower prior precision was associated with lower reciprocal connectivity between these regions (see figure 6A ). In the placebo condition, lower prior precision was also associated with higher levels of gain on the prefrontal and premotor superficial pyramidal neurons whereas in the drug condition lower levels of prior precision were associated only with lower gain on the prefrontal superficial pyramidal neurons (see figure 6B ). Download figure Open in new tab Figure 6 (A) Extrinsic connections and (B) superficial pyramidal gain modulated by prior precision with a posterior probability >0.99. Both extrinsic connectivity and superficial pyramidal gain are shown in placebo and drug conditions in people with FTLD. Discussion There are three key insights from this study. First, we confirm the role of prior precision as a mechanistic explanation for apathy in two disorders associated with frontotemporal lobar degeneration. Second, we show that prior precision can be partially restored with the GABA A agonist zolpidem in people with frontotemporal lobar degeneration. Third, dynamic causal modelling of magnetoencephalography confirmed that the effect of zolpidem may be in part due to changing the postsynaptic gain of prefrontal superficial pyramidal neurons, and subsequent restoration of connectivity in the prefrontal-premotor network. This work highlights the potential for treating apathy by GABAergic restoration in disorders associated with GABA deficits. As expected, people with bvFTD and PSP differed from controls in apathy on all three qualitative measures (AES-Self, AES-Carer & CamQUAIT-M); and had lower levels of prior precision on the task in the placebo condition. This group difference was abolished on zolpidem, primarily driven by an increase in prior precision in patients but not controls. We also confirmed a correlation between apathy and prior precision. The study was underpowered to test this correlation in the patient group alone (i.e. data lacked sufficient precision for Bayesian inference), though trends were in the expected direction and of similar magnitude in both controls and people with FTLD-associated disorders. Dynamic causal modelling of neurophysiological observations supported novel mechanistic insights. The reciprocal connectivity between the prefrontal and premotor cortex, and gain on the prefrontal superficial pyramidal neurons, are particularly relevant correlates of apathy and markers of the zolpidem drug effect. Patients had stronger forward connectivity than controls, but weaker backward connectivity from prefrontal cortex. This suggests that behavioural decisions may be dominated by ascending sensory input without the appropriate top-down modulation according to goals and context 62 . Of particular relevance, the connectivity between the prefrontal and premotor cortex was reduced in patients in the placebo condition but partially restored on zolpidem. In the placebo condition, gain on the superficial pyramidal population was also reduced across all three regions in individuals with FTLD compared to controls. However, this was partially restored on zolpidem, such that prefrontal gain was higher than average in the drug condition. This may be partially driven by a reduction in gain across all three sources in controls on zolpidem, consistent with baseline-dependency by which drug effects are subject to an individual’s baseline. The dynamic causal modelling of MEG resting-state data also confirmed the associations between prior precision (as the index of apathy) and network connectivity both on and off a GABAergic agonist. In patients on placebo, prior precision was negatively associated with reciprocal connectivity between the prefrontal and premotor regions, but the direction of this relationship reversed in the drug condition such that higher levels of prior precision were associated with higher levels of connectivity. Similarly, there was a negative association between prior precision and gain on the superficial pyramidal neurons in both the prefrontal and premotor cortex when on placebo, but this relationship changed on drug such that higher prior precision was associated with higher prefrontal gain. This mirrors healthy elderly controls who demonstrated a similar association between prior precision and prefrontal gain 9 . Together, these results suggest that prior precision may be mediated by gain on superficial pyramidal neurons, which tunes effective communication across the prefrontal-premotor-motor hierarchy, subject to optimal GABAergic innervation. There are several limitations to the current study. Firstly, despite the use of Bayesian statistics confirming sufficient precision (c.f. power) in the majority of our analyses, we were unable to draw inferences either way for some comparisons (1/3 < B <3 indicating insufficient precision in the data, analogous to lack of power for frequentist tests). Variants of the ‘Goal Prior Assay’ may improve power in future studies, to quantify the precision of priors on action outcomes; for example, with additional trials or task simplification to encourage meaningful engagement. Incorporating measures which are not reliant on verbal or manual self-report 63 (e.g. eye tracking) could be an advantage, given the challenges to the interpretation of self-report measures for people with frontotemporal dementia 63 . In addition, the imaging analysis here compares out-of-scanner task performance to in-scanner neurophysiology, albeit on the same study visit. Though we are able to quantify network dynamics and its causes from baseline resting-state analysis, task-based imaging may be used in future studies to further quantify the neural mechanisms at play. There are further limitations relating to the use of zolpidem. Though GABA-A receptors are reduced in individuals with PSP 37 , the evidence is limited and GABAergic deficits in FTLD may be more widespread than can be targeted with zolpidem alone. Moreover, zolpidem acts across the brain, whereas GABAergic deficits are relatively localised in FTLD 38 . This may lead to overdosing of GABA-replete brain regions, resulting in side-effects. Therefore, though this study suggests that GABA agonism may be an effective mechanism to improve prior precision, further research is needed to identify net clinical benefit and examine more selective interventions. Finally, results in people with FTLD may have selection bias (including acceptance bias and completion bias), with more capable individuals completing both the task and resting-state scan. The patient group in the current study therefore represents only a subgroup of the wider patient community, and one with limited diversity. Future research directions include the extension to other conditions in which apathy is highly prevalent, such as Parkinson’s disease, Alzheimer’s disease 64 , 65 , depression and schizophrenia 66 , 67 . It remains to be shown whether prior precision contributes to apathy across all these conditions (noting that it does so in Parkinson’s disease 29 ). It is possible that apathy-associated diseases vary in the functional anatomical changes underlying apathy, or the differential contributions of prior precision, effort avoidance and reward sensitivity 68 , 69 . Exploiting the effect of GABAergic interventions on apathy into clinical trials would also be an important next step, given that the current study was exempted from clinical trials status (in terms of the regulatory framework for clinical trials of interventional medicinal products) as we did not investigate the impact of zolpidem on clinical endpoints. Baseline dependency also remains to be tested as a treatment-decision criterion, implementing a precision medicine approach to treat according to an individual’s GABAergic deficit rather than clinical diagnostic group assignment. In conclusion, we show that apathy in two disorders associated with frontotemporal lobar degeneration is underpinned by the loss of prior precision on action outcomes and a subsequent failure of active inference. This loss of precision is accompanied by a reduction in gain on the prefrontal superficial pyramidal neurons and subsequent functional disconnections across the prefrontal-motor decision-making hierarchy. The GABAA-agonist zolpidem partially restored prior precision, and frontal network connectivity, opening potential new approaches to treat apathy. Data availability Data supporting the work in this publication is available on request from the corresponding author. Fully anonymized, post-processing MEG data is available on request. Clinical metadata are also available on request but will likely require a data transfer agreement to comply with confidentiality and consent. Data Availability Data supporting the work in this publication is available on request from the corresponding author. Fully anonymized, post-processing MEG data is available on request. Clinical metadata are also available on request but will likely require a data transfer agreement to comply with confidentiality and consent. Funding This work has been funded by the Cambridge Trust, the Medical Research Council (MC_UU_00030/14; MR/T033371/1), the Wellcome Trust (220258), the NIHR Cambridge Biomedical Research Centre (NIHR203312), the Cambridge Centre for Parkinson-plus and the Holt fellowship Cambridge Home and EU Scholarship Scheme, James F. McDonnell Foundation, and Evelyn Trust. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. References 1. ↵ Murley AG , Coyle-Gilchrist I , Rouse MA , et al. Redefining the multidimensional clinical phenotypes of frontotemporal lobar degeneration syndromes . Brain J Neurol . 2020 ; 143 ( 5 ): 1555 – 1571 . doi: 10.1093/brain/awaa097 OpenUrl CrossRef PubMed 2. ↵ Robert P , Lanctôt KL , Agüera-Ortiz L , et al. Is it time to revise the diagnostic criteria for apathy in brain disorders? The 2018 international consensus group . Eur Psychiatry J Assoc Eur Psychiatr. 2018 ; 54 : 71 – 76 . doi: 10.1016/j.eurpsy.2018.07.008 OpenUrl CrossRef PubMed 3. ↵ Murley AG , Rouse MA , Coyle-Gilchrist ITS , et al. Predicting loss of independence and mortality in frontotemporal lobar degeneration syndromes . J Neurol Neurosurg Psychiatry . 2021 ; 92 ( 7 ): 737 – 744 . doi: 10.1136/jnnp-2020-324903 OpenUrl Abstract / FREE Full Text 4. ↵ Lansdall CJ , Coyle-Gilchrist ITS , Vázquez Rodríguez P , et al. Prognostic importance of apathy in syndromes associated with frontotemporal lobar degeneration . Neurology . 2019 ; 92 ( 14 ): e1547 – e1557 . doi: 10.1212/WNL.0000000000007249 OpenUrl CrossRef PubMed 5. ↵ Chang CYM , Baber W , Dening T , Yates J . “He Just Doesn’t Want to Get Out of the Chair and Do It”: The Impact of Apathy in People with Dementia on Their Carers . Int J Environ Res Public Health . 2021 ; 18 ( 12 ): 6317 . doi: 10.3390/ijerph18126317 OpenUrl CrossRef PubMed 6. ↵ Silverman HE , Ake JM , Manoochehri M , et al. The contribution of behavioral features to caregiver burden in FTLD spectrum disorders . Alzheimers Dement. n/a(n/a). doi: 10.1002/alz.12494 OpenUrl CrossRef 7. ↵ Fieldhouse JLP , van Dijk G , Gillissen F , et al. A caregiver’s perspective on clinically relevant symptoms in behavioural variant frontotemporal dementia: tools for disease management and trial design . Psychogeriatrics . 2023 ; 23 ( 1 ): 11 – 22 . doi: 10.1111/psyg.12898 OpenUrl CrossRef PubMed 8. ↵ van Paassen D , Hartog L , de Boer S , et al. Expert opinions on pharmacological symptomatic treatment of behavioral symptoms in frontotemporal dementia: A survey of the Neuropsychiatric International Consortium on Frontotemporal Dementia (NIC-FTD) . Eur J Neurol . 2025 ; 32 ( 1 ): e16537 . doi: 10.1111/ene.16537 OpenUrl CrossRef PubMed 9. ↵ Williams RS , Naessens M , Jafarian A , et al. Apathy as a Loss of Prior Precision on Action Outcomes . bioRxiv . Preprint posted online October 7, 2025 : 2025.10.06.680685 . doi: 10.1101/2025.10.06.680685 OpenUrl Abstract / FREE Full Text 10. ↵ Friston K , Thornton C , Clark A . Free-energy minimization and the dark-room problem . Front Psychol . 2012 ; 3 : 130 . doi: 10.3389/fpsyg.2012.00130 OpenUrl CrossRef PubMed 11. Friston K , Kilner J , Harrison L . A free energy principle for the brain . J Physiol Paris . 2006 ; 100 ( 1-3 ): 70 – 87 . doi: 10.1016/j.jphysparis.2006.10.001 OpenUrl CrossRef PubMed Web of Science 12. ↵ Kiebel SJ , David O , Friston KJ . Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization . NeuroImage . 2006 ; 30 ( 4 ): 1273 – 1284 . doi: 10.1016/j.neuroimage.2005.12.055 OpenUrl CrossRef PubMed Web of Science 13. ↵ Friston K , FitzGerald T , Rigoli F , Schwartenbeck P , Pezzulo G . Active Inference: A Process Theory . Neural Comput . 2017 ; 29 ( 1 ): 1 – 49 . doi: 10.1162/NECO_a_00912 OpenUrl CrossRef PubMed 14. ↵ Hezemans FH , Wolpe N , Rowe JB . Apathy is associated with reduced precision of prior beliefs about action outcomes . J Exp Psychol Gen . 2020 ; 149 ( 9 ): 1767 – 1777 . doi: 10.1037/xge0000739 OpenUrl CrossRef 15. ↵ O’Callaghan C , Hezemans FH , Ye R , et al. Locus coeruleus integrity and the effect of atomoxetine on response inhibition in Parkinson’s disease . Brain J Neurol . 2021 ; 144 ( 8 ): 2513 – 2526 . doi: 10.1093/brain/awab142 OpenUrl CrossRef PubMed 16. ↵ Azhar L , Kusumo RW , Marotta G , Lanctôt KL , Herrmann N . Pharmacological Management of Apathy in Dementia . CNS Drugs . 2022 ; 36 ( 2 ): 143 – 165 . doi: 10.1007/s40263-021-00883-0 OpenUrl CrossRef PubMed 17. ↵ Nisar M , Abubaker ZJ , Nizam MA , et al. Behavioral and Cognitive Response to Selective Serotonin Reuptake Inhibitors in Frontotemporal Lobar Degeneration: A Systematic Review and Meta-analysis . Clin Neuropharmacol . 2021 ; 44 ( 5 ): 175 – 183 . doi: 10.1097/WNF.0000000000000469 OpenUrl CrossRef PubMed 18. Deakin JB , Rahman S , Nestor PJ , Hodges JR , Sahakian BJ . Paroxetine does not improve symptoms and impairs cognition in frontotemporal dementia: a double-blind randomized controlled trial . Psychopharmacology (Berl) . 2004 ; 172 ( 4 ): 400 – 408 . doi: 10.1007/s00213-003-1686-5 OpenUrl CrossRef PubMed 19. ↵ Callegari I , Mattei C , Benassi F , et al. Agomelatine Improves Apathy in Frontotemporal Dementia . Neurodegener Dis . 2016 ; 16 ( 5-6 ): 352 – 357 . doi: 10.1159/000445873 OpenUrl CrossRef PubMed 20. ↵ Masdrakis VG , Markianos M , Baldwin DS . Apathy associated with antidepressant drugs: a systematic review . Acta Neuropsychiatr. Published online January 16 , 2023 : 1 – 16 . doi: 10.1017/neu.2023.6 OpenUrl CrossRef 21. ↵ Bogdan A , Manera V , Koenig A , David R . Pharmacologic Approaches for the Management of Apathy in Neurodegenerative Disorders . Front Pharmacol . 2020 ; 10 : 1581 . doi: 10.3389/fphar.2019.01581 OpenUrl CrossRef PubMed 22. Nave S , Doody RS , Boada M , et al. Sembragiline in Moderate Alzheimer’s Disease: Results of a Randomized, Double-Blind, Placebo-Controlled Phase II Trial (MAyflOwer RoAD) . J Alzheimers Dis JAD . 2017 ; 58 ( 4 ): 1217 – 1228 . doi: 10.3233/JAD-161309 OpenUrl CrossRef PubMed 23. ↵ Castrioto A , Thobois S , Anheim M , et al. A randomized controlled double-blind study of rotigotine on neuropsychiatric symptoms in de novo PD . NPJ Park Dis . 2020 ; 6 : 41 . doi: 10.1038/s41531-020-00142-x OpenUrl CrossRef 24. ↵ David MCB , Giovane MD , Liu KY , et al. Cognitive and neuropsychiatric effects of noradrenergic treatment in Alzheimer’s disease: systematic review and meta-analysis . J Neurol Neurosurg Psychiatry . 2022 ; 93 ( 10 ): 1080 – 1090 . doi: 10.1136/jnnp-2022-329136 OpenUrl Abstract / FREE Full Text 25. Mintzer J , Lanctôt KL , Scherer RW , et al. Effect of Methylphenidate on Apathy in Patients With Alzheimer Disease: The ADMET 2 Randomized Clinical Trial . JAMA Neurol . 2021 ; 78 ( 11 ): 1324 – 1332 . doi: 10.1001/jamaneurol.2021.3356 OpenUrl CrossRef PubMed 26. ↵ Clark ED , Perin J , Herrmann N , et al. Effects of methylphenidate on neuropsychiatric symptoms in Alzheimer’s disease: Evidence from the ADMET 2 study . Alzheimers Dement N Y N . 2023 ; 9 ( 3 ): e12403 . doi: 10.1002/trc2.12403 OpenUrl CrossRef 27. ↵ Borderies N , Bornert P , Gilardeau S , Bouret S . Pharmacological evidence for the implication of noradrenaline in effort . PLOS Biol . 2020 ; 18 ( 10 ): e3000793 . doi: 10.1371/journal.pbio.3000793 OpenUrl CrossRef PubMed 28. ↵ Lawson RP , Bisby J , Nord CL , Burgess N , Rees G . The Computational, Pharmacological, and Physiological Determinants of Sensory Learning under Uncertainty . Curr Biol . 2021 ; 31 ( 1 ): 163 – 172 .e4. doi: 10.1016/j.cub.2020.10.043 OpenUrl CrossRef PubMed 29. ↵ Hezemans FH , Wolpe N , O’Callaghan C , et al. Noradrenergic deficits contribute to apathy in Parkinson’s disease through the precision of expected outcomes . PLoS Comput Biol . 2022 ; 18 ( 5 ): e1010079 . doi: 10.1371/journal.pcbi.1010079 OpenUrl CrossRef PubMed 30. ↵ Aoki C , Venkatesan C , Kurose H . Noradrenergic modulation of the prefrontal cortex as revealed by electron microscopic immunocytochemistry . Adv Pharmacol San Diego Calif . 1998 ; 42 : 777 – 780 . doi: 10.1016/s1054-3589(08)60862-5 OpenUrl CrossRef 31. Bennett BD , Huguenard JR , Prince DA . Adrenergic modulation of GABAA receptor-mediated inhibition in rat sensorimotor cortex . J Neurophysiol . 1998 ; 79 ( 2 ): 937 – 946 . doi: 10.1152/jn.1998.79.2.937 OpenUrl CrossRef PubMed Web of Science 32. Kawaguchi Y , Shindou T . Noradrenergic excitation and inhibition of GABAergic cell types in rat frontal cortex . J Neurosci Off J Soc Neurosci . 1998 ; 18 ( 17 ): 6963 – 6976 . doi: 10.1523/JNEUROSCI.18-17-06963.1998 OpenUrl Abstract / FREE Full Text 33. Xing B , Li YC , Gao WJ . Norepinephrine versus Dopamine and their Interaction in Modulating Synaptic Function in the Prefrontal Cortex . Brain Res . 2016 ; 1641 ( Pt B ): 217 – 233 . doi: 10.1016/j.brainres.2016.01.005 OpenUrl CrossRef PubMed 34. ↵ Arnsten AFT . Stress signalling pathways that impair prefrontal cortex structure and function . Nat Rev Neurosci . 2009 ; 10 ( 6 ): 410 – 422 . doi: 10.1038/nrn2648 OpenUrl CrossRef PubMed Web of Science 35. ↵ Ferrer I . Neurons and their dendrites in frontotemporal dementia . Dement Geriatr Cogn Disord . 1999 ; 10 Suppl 1 : 55 – 60 . doi: 10.1159/000051214 OpenUrl CrossRef PubMed 36. ↵ Gascon E , Lynch K , Ruan H , et al. Alterations in microRNA-124 and AMPA receptors contribute to social behavioral deficits in frontotemporal dementia . Nat Med . 2014 ; 20 ( 12 ): 1444 – 1451 . doi: 10.1038/nm.3717 OpenUrl CrossRef PubMed 37. ↵ Suzuki M , Desmond TJ , Albin RL , Frey KA . Cholinergic vesicular transporters in progressive supranuclear palsy . Neurology . 2002 ; 58 ( 7 ): 1013 – 1018 . doi: 10.1212/wnl.58.7.1013 OpenUrl Abstract / FREE Full Text 38. ↵ Murley AG , Rouse MA , Jones PS , et al. GABA and glutamate deficits from frontotemporal lobar degeneration are associated with disinhibition . Brain . 2020 ; 143 ( 11 ): 3449 – 3462 . doi: 10.1093/brain/awaa305 OpenUrl CrossRef PubMed 39. ↵ Adams NE , Hughes LE , Rouse MA , et al. GABAergic cortical network physiology in frontotemporal lobar degeneration . Brain J Neurol . 2021 ; 144 ( 7 ): 2135 – 2145 . doi: 10.1093/brain/awab097 OpenUrl CrossRef PubMed 40. ↵ Benussi A , Premi E , Gazzina S , et al. Neurotransmitter imbalance dysregulates brain dynamic fluidity in frontotemporal degeneration . Neurobiol Aging . 2020 ; 94 : 176 – 184 . doi: 10.1016/j.neurobiolaging.2020.05.017 OpenUrl CrossRef PubMed 41. ↵ Delorme C , Adanyeguh I , Bendetowicz D , et al. Multimodal neurometabolic investigation of the effects of zolpidem on leukoencephalopathy-related apathy . Eur J Neurol . 2020 ; 27 ( 11 ): 2297 – 2302 . doi: 10.1111/ene.14465 OpenUrl CrossRef PubMed 42. ↵ Miyazaki Y , Sako W , Asanuma K , Izumi Y , Miki T , Kaji R . Efficacy of zolpidem for dystonia: a study among different subtypes . Front Neurol . 2012 ; 3 : 58 . doi: 10.3389/fneur.2012.00058 OpenUrl CrossRef PubMed 43. ↵ Whyte J , Rajan R , Rosenbaum A , et al. Zolpidem and restoration of consciousness . Am J Phys Med Rehabil . 2014 ; 93 ( 2 ): 101 – 113 . doi: 10.1097/PHM.0000000000000069 OpenUrl CrossRef PubMed 44. ↵ Autret K , Arnould A , Mathieu S , Azouvi P . Transient improvement of poststroke apathy with zolpidem: a single-case, placebo-controlled double-blind study . BMJ Case Rep . 2013 ; 2013 : bcr2012007816 . doi: 10.1136/bcr-2012-007816 OpenUrl Abstract / FREE Full Text 45. ↵ Brefel-Courbon C , Payoux P , Ory F , et al. Clinical and imaging evidence of zolpidem effect in hypoxic encephalopathy . Ann Neurol . 2007 ; 62 ( 1 ): 102 – 105 . doi: 10.1002/ana.21110 OpenUrl CrossRef PubMed Web of Science 46. ↵ Xu NZ , Ernst M , Treven M , et al. Negative allosteric modulation of alpha 5-containing GABAA receptors engenders antidepressant-like effects and selectively prevents age-associated hyperactivity in tau-depositing mice . Psychopharmacology (Berl ) . 2018 ; 235 ( 4 ): 1151 – 1161 . doi: 10.1007/s00213-018-4832-9 OpenUrl CrossRef 47. ↵ Dash SK . Zolpidem in Progressive Supranuclear Palsy . Case Rep Neurol Med . 2013 ; 2013 : 250865 . doi: 10.1155/2013/250865 OpenUrl CrossRef PubMed 48. ↵ Rascovsky K , Hodges JR , Knopman D , et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia . Brain J Neurol . 2011 ; 134 (Pt 9 ): 2456 – 2477 . doi: 10.1093/brain/awr179 OpenUrl CrossRef PubMed Web of Science 49. ↵ Höglinger GU , Respondek G , Stamelou M , et al. Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria . Mov Disord Off J Mov Disord Soc . 2017 ; 32 ( 6 ): 853 – 864 . doi: 10.1002/mds.26987 OpenUrl CrossRef PubMed 50. Vaghari D , Bruna R , Hughes LE , et al. A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset . NeuroImage . 2022 ; 258 : 119344 . doi: 10.1016/j.neuroimage.2022.119344 OpenUrl CrossRef PubMed 51. ↵ Dickie DA , Shenkin SD , Anblagan D , et al. Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging . Front Neuroinformatics . 2017 ; 11 : 1 . doi: 10.3389/fninf.2017.00001 OpenUrl CrossRef 52. ↵ Kass RE , Raftery AE . Bayes Factors . J Am Stat Assoc. 1995 ; 90 ( 430 ): 773 – 795 . doi: 10.1080/01621459.1995.10476572 OpenUrl CrossRef PubMed Web of Science 53. ↵ van Doorn J , van den Bergh D , Böhm U , et al. The JASP guidelines for conducting and reporting a Bayesian analysis . Psychon Bull Rev . 2021 ; 28 ( 3 ): 813 – 826 . doi: 10.3758/s13423-020-01798-5 OpenUrl CrossRef PubMed 54. ↵ Marin RS , Biedrzycki RC , Firinciogullari S . Reliability and validity of the Apathy Evaluation Scale . Psychiatry Res . 1991 ; 38 ( 2 ): 143 – 162 . doi: 10.1016/0165-1781(91)90040-v OpenUrl CrossRef PubMed Web of Science 55. ↵ Lansdall CJ , Williams R , Coyle-Gilchrist I , et al. The Cambridge Questionnaire for Apathy and Impulsivity Traits (CamQUAIT): a novel assessment tool for frontotemporal lobar degeneration-related syndromes . medRxiv. Preprint posted online July 3, 2024 : 2024.07.01.24309762 . doi: 10.1101/2024.07.01.24309762 OpenUrl Abstract / FREE Full Text 56. ↵ Murley AG , Rowe JB . Neurotransmitter deficits from frontotemporal lobar degeneration . Brain . 2018 ; 141 ( 5 ): 1263 – 1285 . doi: 10.1093/brain/awx327 OpenUrl CrossRef PubMed 57. ↵ Friston KJ , Preller KH , Mathys C , et al. Dynamic causal modelling revisited . Neuroimage . 2019 ; 199 : 730 – 744 . doi: 10.1016/j.neuroimage.2017.02.045 OpenUrl CrossRef PubMed 58. Kiebel SJ , Garrido MI , Moran RJ , Friston KJ . Dynamic causal modelling for EEG and MEG . Cogn Neurodyn . 2008 ; 2 ( 2 ): 121 – 136 . doi: 10.1007/s11571-008-9038-0 OpenUrl CrossRef PubMed 59. ↵ Moran R , Pinotsis D , Friston K . Neural masses and fields in dynamic causal modeling . Front Comput Neurosci . 2013 ; 7 . Accessed March 1, 2022. https://www.frontiersin.org/article/10.3389/fncom.2013.00057 60. ↵ Hughes LE , Rittman T , Robbins TW , Rowe JB . Reorganization of cortical oscillatory dynamics underlying disinhibition in frontotemporal dementia . Brain J Neurol . 2018 ; 141 ( 8 ): 2486 – 2499 . doi: 10.1093/brain/awy176 OpenUrl CrossRef PubMed 61. ↵ Zeidman P , Jafarian A , Seghier ML , et al. A guide to group effective connectivity analysis, part 2: Second level analysis with PEB . NeuroImage . 2019 ; 200 : 12 – 25 . doi: 10.1016/j.neuroimage.2019.06.032 OpenUrl CrossRef PubMed 62. ↵ Kiebel SJ , Daunizeau J , Friston KJ . A Hierarchy of Time-Scales and the Brain. Sporns O, ed . PLoS Comput Biol . 2008 ; 4 ( 11 ): e1000209 . doi: 10.1371/journal.pcbi.1000209 OpenUrl CrossRef PubMed 63. ↵ Williams RS , Adams NE , Hughes LE , et al. Syndromes associated with frontotemporal lobar degeneration change response patterns on visual analogue scales . Sci Rep . 2023 ; 13 ( 1 ): 8939 . doi: 10.1038/s41598-023-35758-5 OpenUrl CrossRef PubMed 64. ↵ Leung DKY , Chan WC , Spector A , Wong GHY . Prevalence of depression, anxiety, and apathy symptoms across dementia stages: A systematic review and meta-analysis . Int J Geriatr Psychiatry . 2021 ; 36 ( 9 ): 1330 – 1344 . doi: 10.1002/gps.5556 OpenUrl CrossRef PubMed 65. ↵ Teixeira AL , Gonzales MM , de Souza LC , Weisenbach SL . Revisiting Apathy in Alzheimer’s Disease: From Conceptualization to Therapeutic Approaches . Behav Neurol . 2021 ; 2021 : e6319826 . doi: 10.1155/2021/6319826 OpenUrl CrossRef 66. ↵ Yuen GS , Gunning FM , Woods E , Klimstra SA , Hoptman MJ , Alexopoulos GS . Neuroanatomical correlates of apathy in late-life depression and antidepressant treatment response . J Affect Disord . 2014 ; 166 : 179 – 186 . doi: 10.1016/j.jad.2014.05.008 OpenUrl CrossRef PubMed 67. ↵ Bortolon C , Macgregor A , Capdevielle D , Raffard S . Apathy in schizophrenia: A review of neuropsychological and neuroanatomical studies . Neuropsychologia . 2018 ; 118 (Pt B ): 22 – 33 . doi: 10.1016/j.neuropsychologia.2017.09.033 OpenUrl CrossRef 68. ↵ Le Bouc R , Borderies N , Carle G , et al. Effort avoidance as a core mechanism of apathy in frontotemporal dementia . Brain. Published online November 19 , 2022 : awac427 . doi: 10.1093/brain/awac427 OpenUrl CrossRef 69. ↵ Levy R , Dubois B . Apathy and the Functional Anatomy of the Prefrontal Cortex–Basal Ganglia Circuits . Cereb Cortex . 2006 ; 16 ( 7 ): 916 – 928 . doi: 10.1093/cercor/bhj043 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted October 24, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following GABAergic regulation of action-outcome priors in conditions associated with frontotemporal degeneration Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share GABAergic regulation of action-outcome priors in conditions associated with frontotemporal degeneration Rebecca S. Williams , Michelle Naessens , Emily G Todd , Robert Durcan , David J Whiteside , Juliette Lanskey , Amirhossein Jafarian , Karl Friston , Laura E. Hughes , James B. Rowe medRxiv 2025.10.22.25338552; doi: https://doi.org/10.1101/2025.10.22.25338552 Share This Article: Copy Citation Tools GABAergic regulation of action-outcome priors in conditions associated with frontotemporal degeneration Rebecca S. Williams , Michelle Naessens , Emily G Todd , Robert Durcan , David J Whiteside , Juliette Lanskey , Amirhossein Jafarian , Karl Friston , Laura E. Hughes , James B. Rowe medRxiv 2025.10.22.25338552; doi: https://doi.org/10.1101/2025.10.22.25338552 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15227) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6597) Geriatric Medicine (668) Health Economics (997) Health Informatics (4534) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9230) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a002f98a9b088650',t:'MTc3OTUyNzkyMg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();