Early brain-wide disruption of sleep microarchitecture in Amyotrophic Lateral Sclerosis.

Early brain-wide disruption of sleep microarchitecture in Amyotrophic Lateral Sclerosis | 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 Early brain-wide disruption of sleep microarchitecture in Amyotrophic Lateral Sclerosis Christina Lang , Simon J. Guillot , Dorothee Lule , Luisa T. Balz , Antje Knehr , Patrick Weydt , Johannes Dorst , Katharina Kandler , Hans-Peter Muller , Jan Kassubek , Laura Wassermann , Sandrine Da Cruz , Francesco Roselli , Albert C. Ludolph , Matei Bolborea , Luc Dupuis doi: https://doi.org/10.1101/2025.03.28.25324779 Christina Lang 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simon J. Guillot 3 University of Strasbourg, INSERM, Strasbourg Translational Neuroscience & Psychiatry STEP – CRBS, UMR-S 1329 ; Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dorothee Lule 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luisa T. Balz 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antje Knehr 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Patrick Weydt 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 4 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Bonn, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Johannes Dorst 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katharina Kandler 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hans-Peter Muller 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jan Kassubek 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Wassermann 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sandrine Da Cruz 5 VIB-KU Leuven Center for Brain and Disease Research and Department of Neurosciences , KU Leuven, Leuven 3000, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Francesco Roselli 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Albert C. Ludolph 1 Department of Neurology, University Hospital of Ulm; Ulm , Germany 2 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Ulm, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: albert.ludolph{at}uni-ulm.de mbolborea{at}unistra.fr ldupuis{at}unistra.fr Matei Bolborea 3 University of Strasbourg, INSERM, Strasbourg Translational Neuroscience & Psychiatry STEP – CRBS, UMR-S 1329 ; Strasbourg, France 6 School of Life Sciences, University of Warwick; Gibbet Hill Road , Coventry, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: albert.ludolph{at}uni-ulm.de mbolborea{at}unistra.fr ldupuis{at}unistra.fr Luc Dupuis 3 University of Strasbourg, INSERM, Strasbourg Translational Neuroscience & Psychiatry STEP – CRBS, UMR-S 1329 ; Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: albert.ludolph{at}uni-ulm.de mbolborea{at}unistra.fr ldupuis{at}unistra.fr Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Amyotrophic lateral sclerosis (ALS), the major adult-onset motor neuron disease, is preceded by an early period unrelated to the motor system, including altered sleep, with increased wake and decreased NREM sleep phases. Whether these alterations in sleep macroarchitecture are associated, or even preceded by abnormalities in sleep-related EEG hallmarks is unknown. Here we used polysomnography to characterize sleep microarchitecture in the early phases of ALS. We observed a brain-wide decrease in density of sleep spindles, slow oscillations and k-complexes, three sleep-related EEG signals, in both early-stage ALS patients and presymptomatic gene carriers. These alterations in sleep spindles were correlated with cognitive performance, particularly of scores in memory, verbal fluency and speech, in both cohorts. Importantly, alterations in sleep microarchitecture were replicated in 3 mouse models and decreases in sleep spindles were rescued by MCH intracerebroventricular supplementation or oral administration of a dual orexin receptor antagonist. Thus, sleep microarchitecture is associated with cognitive deficits and causally related to aberrant MCH and orexin signaling in ALS. Introduction Amyotrophic lateral sclerosis (ALS) is a fatal and rapidly progressive disease affecting upper and lower motor neurons in adults, with a median survival of three to four years after onset of motor symptoms. Onset occurs usually between 60 and 70 years of age 1 , 2 and most cases do not show a family history. However 5-10% of ALS cases are familial cases, with more than 40 distinct genes currently associated, and mutations in C9ORF72, SOD1 , TARDBP and FUS , as major genetic causes of ALS 1 , 2 . It is generally considered that ALS patients do not show clinical manifestations before the onset of symptoms. As a matter of fact, the increase in circulating neurofilament levels, that is considered a reliable biomarker of (motor) axonal injury, is observed at onset of motor symptoms but not one to two years before onset 3 , 4 . Clinically, most presymptomatic gene carriers do not show even mild motor impairment 2 years before onset of motor symptoms 4 , 5 . However, future ALS patients show a number of early non-motor signs, such as weight loss 6 – 10 or cognitive impairment 11 , 12 , many years before onset of motor symptoms. Recently, we identified sleep alterations as a novel early non-motor sign. We studied sleep in two cohorts of ALS patients devoid of respiratory insufficiency and presymptomatic gene carriers. In both cohorts, we observed significant defects in sleep microarchitecture characterized by increased wake and decreased deep sleep (NREM2/3) 13 , that were detectable at least 10-15 years before the anticipated onset of motor symptoms in presymptomatic gene carriers. Importantly, the severity of alterations in sleep correlated with cognitive scores 13 and were mirrored by mouse models of familial ALS 13 . While this study established sleep defects as an early phenotype in future ALS patients, we did not characterize how sleep-related EEG hallmarks were affected in ALS. The different sleep states are characterized by various alterations in EEG recordings that are related to the activation of specific cortical and subcortical pathways and constitute sleep microarchitecture. These EEG hallmarks of sleep include sleep spindles, a burst of 12-15 Hz sinusoidal cycles in EEG 14 , as well as slow oscillations (<1Hz) and K-complexes, that are all involved in NREM sleep continuity and the role of sleep in memory consolidation and cognitive function 14 , 15 . A defect in sleep microarchitecture could be caused by specific neuroanatomical pathways, and also serve as a possible biomarker for sleep deficits. Here, we characterized sleep microarchitecture components in ALS patients, presymptomatic gene carriers and mouse models 13 . We observed a brain wide decrease in sleep spindles, slow oscillations and K-complexes, that were correlated to cognitive function. We also observed similar alterations in mouse models and showed their rescue by MCH supplementation or a dual-orexin receptor antagonist. Thus, sleep microarchitecture dynamics are early affected in ALS and relates to hypothalamic dysfunction. Results Early ALS patients exhibit brain-wide decreased sleep spindles density To examine the extent of alterations in sleep microarchitecture in individuals with early-stage amyotrophic lateral sclerosis (ALS), we took advantage of polysomnography acquired in a previous cohort study 13 . Characteristics of included patients are provided in Table 1 . Importantly, we prospectively excluded patients and controls with abnormal capnography, thus ruling out that the observed sleep defects were secondary to respiratory insufficiency. No significant differences were observed between the ALS patients and the control group with regard to age, sex or body mass index. View this table: View inline View popup Download powerpoint Table 1. Descriptive statistics of the study population of ALS patients and healthy controls. (SEM: standard error of means; BMI: body mass index; ALSFRSr: Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised; ns p value >0.05, non-parametric Kruskal-Wallis’ test). Consistent with the previously observed strong defect in NREM2 and 3, we observed a pronounced decrease in sleep spindle density and their root mean square in ALS patients ( Figure 1A-B ), while their amplitude was unchanged ( Figure 1B ). Topographically, we observed a significant decrease in density in both frontal and motor cortex electrodes ( Figure 1C ) while the root mean square of sleep spindles was only significantly decreased in motor cortex electrodes (C3/C4) but not in frontal cortex electrodes (F3/F4) ( Figure 1C ). Consistent with decreased sleep spindles density, we observed similar brain-wide reductions in slow oscillations and K-complexes in individuals with early-stage ALS ( Supplementary Figures 1 and 2 ). Thus sleep microarchitecture is altered in early-stage ALS patients at the whole brain level. Download figure Open in new tab Supplementary Figure 1: Slow oscillations alterations in early ALS patients. (A) Representative slow oscillations across all electrodes of a healthy individual and one sporadic ALS patient. (B) Quantification of slow oscillation density, slope, phase-amplitude coupling between sleep spindles and slow oscillations (PAC) and phase at Sigma peak (PSP) in controls and ALS patients. Men are shown in green and women in purple. (C) Topographic maps across all electrodes of slow oscillation density, slope, phase-amplitude coupling and phase at Sigma peak in Controls (upper row) and ALS patients (lower row). (D) Quantification of slow oscillation density, slope, phase-amplitude coupling and phase at Sigma peak across F3/F4 and C3/C4 electrodes as indicated. Only p values <0.05 are shown. Data are presented as median and interquartile ranges. Corrected p value are shown. Download figure Open in new tab Supplementary Figure 2: K-complexes alterations in early ALS patients. (A) Topographic map across all electrodes of K-complex density in controls and ALS patients. (B) Quantification of K-complex density in controls and ALS patients. Men are shown in green and women in purple. (C) Quantification of K- complex density across F3/F4 and C3/C4 electrodes as indicated. Download figure Open in new tab Figure 1: Sleep spindles alterations in early ALS patients. (A) Topographic map across all electrodes of sleep spindle density in controls and ALS patients. (B) Quantification of sleep spindle density, amplitude and root-mean-square (RMS). Men are shown in green and women in purple. (C) Quantification of sleep spindle density, amplitude and root-mean-square (RMS) across F3/F4 and C3/C4 electrodes as indicated. Results with p value >0.05 are not indicated. Data are presented as median and interquartile ranges. Corrected p value are shown. Presymptomatic ALS gene carriers show decreased sleep spindles density To further characterize sleep microarchitecture defects, we then assessed it in a second prospective cohort study comprising presymptomatic ALS gene carriers, utilising identical inclusion and exclusion criteria. This cohort corresponds to the cohort previously characterized in 13 , with additional 29 gene carriers and 11 non gene carriers. A total of 57 presymptomatic gene carriers ( SOD1 n=13; C9orf72 n=33) and 30 first-degree non-carriers relatives ( Table 2 ). Similarly to ALS patients, presymptomatic SOD1 and C9ORF72 ALS gene carriers demonstrated comparable reductions in sleep spindle density and root mean square ( Figure 2A-B ), with unchanged mean brain-wide amplitude ( Figure 2B ). Density and root mean square of sleep spindles were decreased in both SOD1 and C9ORF72 ALS gene carriers in both frontal and motor cortex relevant electrodes ( Figure 2C ). Interestingly, SOD1, but not C9ORF72, gene carriers also displayed a mildly decreased amplitude in frontal and motor areas ( Figure 2D-F ). Similar to ALS patients, a brain-wide reduction in slow oscillations and K-complexes was observed in presymptomatic gene carriers ( Supplementary Figures 3 and 4 ). Thus sleep microarchitecture is already strongly affected in presymptomatic ALS-gene carriers. Download figure Open in new tab Supplementary Figure 3: Slow oscillations alterations in presymptomatic ALS gene carriers. (A) Representative slow oscillations across all electrodes of a healthy individual and one presymptomatic SOD1 and C9ORF72 gene carrier. (B) Quantification of slow oscillation density, slope, phase-amplitude coupling between sleep spindles and slow oscillations (PAC) and phase at Sigma peak (PSP) in controls, SOD1 and C9ORF72 presymptomatic gene carriers. Men are shown in green and women in purple. (C) Topographic maps across all electrodes of slow oscillation density, slope, phase-amplitude coupling and phase at Sigma peak in controls, SOD1 and C9ORF72 presymptomatic gene carriers. (D) Quantification of slow oscillation density, slope, phase-amplitude coupling and phase at Sigma peak across F3/F4 and C3/C4 electrodes in controls, SOD1 and C9ORF72 presymptomatic gene carriers as indicated. Download figure Open in new tab Supplementary Figure 4: K-complexes alterations in presymptomatic ALS gene carriers. (A) Topographic map across all electrodes of K-complex density in controls, SOD1 and C9ORF72 presymptomatic gene carriers. (B) Quantification of K-complex density in controls, SOD1 and C9ORF72 presymptomatic gene carriers. Men are shown in green and women in purple. (C) Quantification of K- complex density across F3/F4 and C3/C4 electrodes in controls, SOD1 and C9ORF72 presymptomatic gene carriers as indicated. Download figure Open in new tab Figure 2: Sleep spindles alterations in presymptomatic ALS gene carriers. (A) Topographic map across all electrodes of sleep spindle density in controls, SOD1 and C9ORF72 presymptomatic gene carriers. (B) Quantification of sleep spindle density, amplitude and root-mean-square (RMS) in controls, SOD1 and C9ORF72 presymptomatic gene carriers. Men are shown in green and women in purple. (C) Quantification of sleep spindle density, amplitude and root-mean-square (RMS) in controls, SOD1 and C9ORF72 presymptomatic gene carriers across F3/F4 and C3/C4 electrodes as indicated. Results with p value >0.05 are not indicated. Data are presented as median and interquartile ranges. Corrected p value are shown. View this table: View inline View popup Download powerpoint Table 2. Descriptive statistics of the study population of fALS participants. (SEM: standard error of means; BMI: body mass index. Non-parametric Kruskal-Wallis’ test. Microarchitectural alterations of sleep patterns correlate with cognitive deficits Alterations in sleep microarchitecture are commonly associated with cognitive deficits 14 , 15 . To determine whether this would be the case in our two cohorts, we correlated sleep microarchitecture parameters with cognitive function in both ALS patients and presymptomatic gene carriers, and with motor function in ALS patients. Cognitive function was evaluated using the Edinburgh Cognitive and Behavioural ALS Screen (ECAS) and motor function with the revised ALS functional rating scale (ALS-FRSr). In ALS patients, a significant positive correlation after adjustment for multiple comparisons ( Figure 3A , Supplementary Table 1 ) was observed between sleep spindle density and memory, speech and verbal fluency subscores, as well as the total ECAS score, but not with the ALS-FRS score or its slope ( Figure 3A-E ). Similar correlations, although weaker, were observed in presymptomatic gene carriers between sleep spindle density and memory and executive functions subscores as well as the total ECAS score ( Figure 3F-H , Supplementary Table 2 ). Similarly, positive correlations were identified between slow oscillations and total or ECAS subscores, in ALS patients and presymptomatic gene carriers ( Supplementary Figure 5 - 6 ). There were much weaker correlations between cognitive evaluation results and K-complexes in ALS patients but not presymptomatic gene carriers ( Supplementary Figure 6 ). Therefore, alterations in the microarchitecture of sleep patterns are associated with cognitive performance, particularly in relation to verbal fluency, speech and memory. Download figure Open in new tab Supplementary Figure 5: Correlation analysis between slow oscillations and K- complex density and cognitive function in ALS patients. ( A ) Correlation matrix showing Spearman correlation coefficient r for each of the corresponding correlations performed in ALS patients. Slow oscillation or K-complex density were correlated with ALSFRSr, ALSFRS slope, patients’ survival and ECAS subscores as well as the total score for all ALS patients. Only significant correlations are indicated, with the numerical value of the Spearman r . ( B-E ) Correlation between slow oscillation density and ECAS memory subscore ( B ) or verbal fluency subscore ( C ), speech subscore ( D) or total ECAS score ( E ). ( F-G ) Correlation between K-complex density and ECAS speech subscore ( F) or total ECAS score ( G ). In all panels, men are shown in green and women in purple. Spearman pvalue was adjusted with FDR-BKY correction. Spearman correlation coefficient r and corrected pvalue are indicated. Side distribution represents sex distribution across both variables (men in green, women in purple). Download figure Open in new tab Supplementary Figure 6: Correlation analysis between slow oscillations and K- complex density and cognitive function in presymptomatic ALS gene carriers. ( A ) Correlation matrix showing Spearman correlation coefficient r for each of the corresponding correlations performed in presymptomatic gene carriers. Slow oscillation and K-complex densities were correlated with ECAS subscores as well as the total score for all SOD1 and C9ORF72 gene carriers. Only significant correlations are indicated, with the numerical value of the Spearman r . ( B-D ) Correlation between slow oscillation density and ECAS verbal fluency subscore ( B ), speech subscore ( C ) or total ECAS score ( D ) in presymptomatic gene carriers. In all panels, men are shown in green and women in purple. Spearman pvalue was adjusted with FDR-BKY correction. Spearman correlation coefficient r and corrected pvalue are indicated. Side distribution represents sex distribution across both variables (men in green, women in purple). View this table: View inline View popup Download powerpoint Supplementary Table 1: correlation between sleep microarchitecture and clinical parameters in ALS patients Spearman r and adjusted p-values are indicated for each comparison. View this table: View inline View popup Download powerpoint Supplementary Table 2: correlation between sleep microarchitecture and clinical parameters in presymptomatic gene carriers Spearman r and adjusted p-values are indicated for each comparison. Download figure Open in new tab Figure 3: Correlation analysis between sleep microarchitecture and cognitive function in ALS patients. ( A ) Correlation matrix showing Spearman correlation coefficient r for each of the corresponding correlations performed in ALS patients. Sleep spindle density was correlated with ALSFRSr, ALSFRS slope, patients’ survival and ECAS subscores as well as the total score for all ALS patients. Only significant correlations are indicated, with the numerical value of the Spearman r . ( B-E ) Correlation between sleep spindle density and ECAS memory subscore ( B ) or speech subscore ( C ), verbal fluency subscore ( D) or total ECAS score ( E ). ( F ) Correlation matrix showing Spearman correlation coefficient r for each of the corresponding correlations performed in presymptomatic gene carriers. Sleep spindle density was correlated with ECAS subscores as well as the total score for all SOD1 and C9ORF72 gene carriers. Only significant correlations are indicated, with the numerical value of the Spearman r . ( G-H ) Correlation between sleep spindle density and ECAS memory subscore ( G ) or total ECAS score ( H ) in presymptomatic gene carriers. In all panels, men are shown in green and women in purple. Spearman pvalue was adjusted with FDR-BKY correction. Spearman correlation coefficient r and corrected pvalue are indicated. Side distribution represents sex distribution across both variables (men in green, women in purple). Three ALS models exhibit microarchitectural alterations of sleep patterns Given the presence of sleep microarchitecture alterations in both early ALS patients and presymptomatic gene carriers, we sought to investigate whether these microarchitectural alterations are mirrored by findings in transgenic ALS mouse models. We studied three models expressing different ALS-causing mutations and markedly disparate disease progression. The transgenic model Sod1 G86R is associated with severe and rapidly progressive motor symptoms 16 , 17 , whereas the Fus ΔNLS/+ 18 , 19 or TDP-43 Q331K models 20 are linked to a light-to-mild and late-onset phenotype 13 . We used datasets previously acquired 13 in mouse cohorts implanted with intra-cortical electrodes, and recordings during the presymptomatic phase. As observed in ALS patients and gene carriers, all three models demonstrated substantial alterations in sleep spindles ( Figure 4A-E , Supplementary Figure 7). It is noteworthy that there was a significant decrease of sleep spindle in 3-month-old Fus ΔNLS/+ mice ( Figure 4C ), while there was no defect in sleep macroarchitecture identified in these animals 13 . As in ALS patients and presymptomatic gene carriers, decreased sleep spindle density was also accompanied by decreased densities in both slow oscillations and K-complexes ( Supplementary Figure 8 ). In all three models, microarchitectural alterations were consistently observed in both males and females. Thus early sleep microarchitecture alterations similar to those observed in early ALS patients and gene carriers are observed in multiple ALS mouse models. Download figure Open in new tab Supplementary Figure 7: Sleep microarchitecture alterations in Fus Δ NLS/+ and TDP-43 Q331K mice. ( A-C ) Representative spectrogram of mice Fus ΔNLS/+ mice and their WT littermates ( Fus +/+ ) at 3 months of age ( A, prior to motor symptom onset) or at 10 months of age ( B ) and in TDP-43 Q331K mice at 10 months of age ( C ). Sleep spindles are labelled in blue and slow oscillation in red on the spectrogram. Download figure Open in new tab Supplementary Figure 8: additional sleep microarchitecture alterations in Sod1 G86R , Fus ΔNLS/+ and TDP-43 Q331K mice. ( A-B ) Quantification of slow oscillation density ( A ) and K-complex density ( B ) in Sod1 G86R mice and their non-transgenic WT littermates at 75 days of age, in Fus ΔNLS/+ mice and their WT littermates ( Fus +/+ ) at 3 months of age ( prior to motor symptom onset) or at 10 months of age and in TDP-43 Q331K mice at 10 months of age. Independent Student’s t-test with Welch’s t-test with FRD-BKY correction. Data are presented as median and interquartile ranges. Corrected p value are shown. Download figure Open in new tab Figure 4: Sleep microarchitecture alterations in Sod1 G86R , Fus ΔNLS/+ and TDP-43 Q331K mice. ( A ) Representative spectrogram of Sod1 G86R mice and their non-transgenic wild-type (WT) littermates at 75 days of age (prior to motor symptom onset). Sleep spindles are labelled in blue and slow oscillation in red on the spectrogram. ( B-E ) Quantification of sleep spindle density in Sod1 G86R mice and their non-transgenic WT littermates at 75 days of age ( B ), in Fus ΔNLS/+ mice and their WT littermates ( Fus +/+ ) at 3 months of age ( C, prior to motor symptom onset) or at 10 months of age ( D ) and in TDP-43 Q331K mice at 10 months of age ( E ). Independent Student’s t-test with Welch’s t-test with FRD-BKY correction; Data are presented as median and interquartile ranges. Corrected p value are shown. Sleep spindle defects are rescued by an orexin antagonist We previously showed that sleep defects in ALS mouse models can be fully rescued by administration of suvorexant, a dual orexin receptor antagonist. We re-analyzed the previous datasets in which we administered Suvorexant or its vehicle orally at the onset of the inactive period (ie during the day in mice). Acute administration of Suvorexant rescued sleep spindles density in all three mouse models, with a more pronounced effect in females, and decreased efficacy in aged 10-month-old mice ( Figure 5 , Supplementary Figure 9) as well as slow oscillations and K complex loss ( Supplementary Figure 10 ). MCH had similar, yet blunted effects on all sleep microarchitecture parameters in either Sod1 G86R mice ( Supplementary Figure 11 ) or Fus Δ NLS/+ mice ( Supplementary Figure 12 ). In all, our results suggest that increased orexinergic tone is causally related to sleep microarchitectural defects in both ALS mouse models and patients. Download figure Open in new tab Supplementary Figure 9: Sleep microarchitecture alterations in Fus ΔNLS/+ and TDP-43 Q331K mice treated with suvorexant. ( A-C ) Representative spectrogram of mice administered with either vehicle or Suvorexant. Representative spectrograms for indicated genotypes are shown: Fus ΔNLS/+ mice and their WT littermates ( Fus +/+ ) at 3 months of age ( A, prior to motor symptom onset) or at 10 months of age ( B ) and in TDP-43 Q331K mice at 10 months of age ( C ). Sleep spindles are labelled in blue and slow oscillation in red on the spectrogram. Download figure Open in new tab Supplementary Figure 10: additional results on sleep microarchitecture in Sod1 G86R , Fus ΔNLS/+ and TDP-43 Q331K mice treated with suvorexant. ( A-B ) Quantification of slow oscillation density ( A ) and K-complex density ( B ) in Sod1 G86R mice and their non-transgenic WT littermates at 75 days of age, in Fus ΔNLS/+ mice and their WT littermates ( Fus +/+ ) at 3 months of age ( prior to motor symptom onset) or at 10 months of age and in TDP-43 Q331K mice at 10 months of age . Mice were either administered vehicle or suvorexant as indicated. Data are presented as median and interquartile ranges. Corrected p value are shown. Download figure Open in new tab Supplementary Figure 11: Rescued sleep microarchitecture by MCH chronic delivery in Sod1 G86R mice. ( A ) Representative spectrogram of Sod1 G86R mice and their non-transgenic wild-type (WT) littermates at 75 days of age (prior to motor symptom onset) administered with either vehicle or MCH via icv cannulation and osmotic minipump delivery. Sleep spindles are labelled in blue and slow oscillation in red on the spectrogram. ( B-D ) Quantification of sleep spindle density ( B ), slow oscillation density ( C ) and K-complex density ( D ) in Sod1 G86R mice and their non-transgenic WT littermates treated with either vehicle or MCH at 75 days of age Two-Way ANOVA with Dunn’s test and FDR-BKY correction. Data are presented as median and interquartile ranges. Corrected pvalue are shown. Download figure Open in new tab Supplementary Figure 12: Rescued sleep microarchitecture by MCH chronic delivery in Fus ΔNLS/+ mice. ( A ) Representative spectrogram of Fus Δ NLS/+ mice and their non-transgenic wild-type (WT) littermates at 75 days of age (prior to motor symptom onset) administered with either vehicle or MCH via icv cannulation and osmotic minipump delivery. Sleep spindles are labelled in blue and slow oscillation in red on the spectrogram. ( B-D ) Quantification of sleep spindle density ( B ), slow oscillation density ( C ) and K-complex density ( D ) in Fus ΔNLS/+ mice and their WT littermates ( Fus +/+ ) mice treated with either vehicle or MCH at 10 months of age. Two-Way ANOVA with Dunn’s test and FDR-BKY correctionData are presented as median and interquartile ranges. Corrected pvalue are shown. Download figure Open in new tab Figure 5: Rescued sleep microarchitecture by Suvorexant Sod1 G86R , Fus ΔNLS/+ and TDP-43 Q331K mice. (A) Representative spectrogram of Sod1 G86R mice and their non-transgenic wild-type (WT) littermates at 75 days of age (prior to motor symptom onset) administered with either vehicle or Suvorexant. Sleep spindles are labelled in blue and slow oscillation in red on the spectrogram. ( B-E ) Quantification of sleep spindle density in mice treated with either vehicle or suvorexant. Genotypes studied include Sod1 G86R mice and their non-transgenic WT littermates at 75 days of age ( B ), in Fus ΔNLS/+ mice and their WT littermates ( Fus +/+ ) at 3 months of age ( C, prior to motor symptom onset) or at 10 months of age ( D ) and in TDP-43 Q331K mice at 10 months of age ( E ) ns adj. pvalue>0.05, Two-Way ANOVA with Dunn’s test and FDR-BKY correction. Data are presented as median and interquartile ranges. Corrected pvalue are shown. Discussion Here, we show that disruption of sleep microarchitecture is profound, brain-wide and precedes the onset of motor symptoms in ALS, both in humans and in mouse models. Disruption of sleep microarchitecture involves in ALS involves most sleep-related EEG hallmarks, including sleep spindles, slow oscillations and K-complexes. For all these 3 EEG signals, we observed decreased densities in ALS patients without respiratory impairment, but also in presymptomatic ALS gene carriers, as compared to their respective controls. Decreased density was brain-wide, and we did not observe patterns of disruption related to motor or frontal involvement. We also observed a similar alteration in sleep microarchitecture in 3 different mouse models of ALS. Importantly, defects in sleep microarchitecture were stronger and observed earlier in mouse models than macroarchitectural defects 13 . Indeed, while we previously did not observe increased wake or decreased NREM at 3 months of age in Fus ΔNLS/+ mice, there was already at this age a severe loss of sleep spindles, slow oscillations and K-complexes. Disruption of sleep EEG hallmarks, as we describe here in ALS, is widely observed in neurological and neurodegenerative diseases. Loss of sleep spindles, especially decreased density, and of slow oscillation, has been repeatedly observed in Alzheimer’s disease 21 – 23 or Parkinson’s disease 24 – 26 , as well as in other neurological diseases such as temporal lobe epilepsy 27 , schizophrenia 28 – 30 . This is however not a universal feature of neurological impairment as it is not observed in patients with ADHD, PTSD or most patients with autism spectrum disorder 28 . Interestingly, sleep spindle density also decreases with age 31 , permitting the interpretation that accelerated brain aging could account for some of our results in ALS patients either for microarchitecture defects (the current study) or for macroarchitecture defects 13 . Since decreased spindle density is observed in multiple neurological and neurodegenerative conditions, our current observations are likely not of diagnostic relevance. However, sleep spindles and slow oscillations are quantifiable outcomes, and their defects appear very early in disease. It is thus possible that sleep EEG hallmarks should be investigated for prognostic purposes. Our current lack of information on EEG dynamics during phenoconversion and disease progression hampers evaluation of these alterations as prognostic biomarkers. Future studies should include longitudinal polysomnography in presymptomatic gene carriers to determine the kinetics of sleep macro- and micro-architecture and their possible worsening as a prognostic marker of phenoconversion. What could be the consequences of defects in sleep microarchitecture? It is widely documented that sleep spindles and slow oscillations are causally involved in memory consolidation and executive functions 14 , 15 . Indeed, in patients with Parkinson’s or Alzheimer’s diseases, the extent of sleep spindles defects was related to memory and cognitive impairments 21 – 26 . Complementary to this, we observed strong correlations between cognitive scores and sleep spindles or slow oscillations in our two cohorts. These correlations were more robust than correlations between sleep stages and cognitive function previously observed in the same cohorts 13 . It is currently unknown whether loss of sleep spindles might affect motor progression in ALS. It is however noteworthy that sleep spindles are highly correlated with motor adaptation 32 and motor learning 33 – 36 , and it is possible that loss of this functional homeostasis might exacerbate motor progression. Longitudinal studies in patients and gene carriers, as well as experimental studies in rodents might address this question. Sleep spindles originate in the thalamus 14 , 15 , and their disruption in ALS indirectly suggests a disruption of cortico-thalamic networks. This disruption could be direct, and caused by intrinsic alteration of thalamic neurons or thalamo-cortical pathways. Atrophy of the thalamus has been largely documented in ALS, in particular in C9ORF72 patients 37 – 42 , and alterations of thalamo-cortical pathways has been observed in sporadic ALS 43 , 44 and in presymptomatic C9ORF72 gene carriers 45 . These thalamic alterations have been related to faster progression in sporadic ALS 46 and higher risk of phenoconversion in C9ORF72 ALS 47 . Thalamic involvement has been considered an early biomarker of ALS, that contributes to motor and cognitive deficits in sALS 48 – 51 . It is also possible that these defects are indirectly caused by abnormal signaling by hypothalamic neuropeptides, such as orexin. Indeed, thalamic reticular nucleus neurons are sensitive to orexin 52 , 53 , and we provide evidence that orexin antagonism or MCH supplementation are able to rescue the loss of sleep spindles and slow oscillation in several ALS mice. This is consistent with the loss of MCH neurons observed in ALS 54 , and several studies pointing to orexin alterations in ALS patients and mouse models 13 , 55 , 56 . It is also possible that defects in other neuromodulators, in particular acetylcholine 57 – 60 , cause these defects in sleep microarchitecture, which would be consistent with previous observations on acetylcholine defects in ALS 60 . Defects in norepinephrine could also cause these defects in sleep microarchitecture as this neurotransmitter is highly involved in sleep-related cognitive consolidation 61 – 63 . This would be consistent with our previous observations on norepinephrine defects in ALS 64 . Whether defects in sleep microarchitecture directly originate from degeneration or dysfunction of thalamic reticular nucleus neurons or from indirect circuit dysfunction involving direct LHA innervation or other neuronal relays will require further experimental work. Summarizing, our current work establishes disruption of sleep EEG as a very early biomarker of ALS, detectable many years before onset of symptoms in presymptomatic gene carriers and correlated with cognitive impairment. Elucidation of the underlying mechanisms might shed light on the very precocious events in ALS pathophysiology, and the relevance of these alterations for prognosis should be studied longitudinally in prospective cohorts. Materials and Methods Patients/participants ALS patients were recruited from the inpatient and outpatient clinics of the neurologic department of the University Hospital of Ulm, Germany. The inclusion criteria for ALS patients included a diagnosis of definite ALS based on the revised El Escorial criteria[30]. Presymptomatic carriers of fALS genes were recruited through the study centre of the Neurological University Hospital, through which first-degree relatives of confirmed familial ALS patients receive longitudinal follow-up and counselling. Controls were recruited from the general population at the neurology clinic, and matched to ALS patients based on age, sex, and geographical location; the requirement for this group was the exclusion of neurodegenerative diseases. All individuals in the control group were unrelated to ALS or familial ALS. The study in the ALS patient cohort was approved by the Ethics Committee of the University of Ulm (reference 391/18), as well as the study in the presymptomatic carriers which was also approved by the Ethics Committee of the University of Ulm (reference 68/19), in compliance with the ethical standards of the current version of the revised Helsinki Declaration. All participants gave informed consent prior to enrolment. Medical history was documented. For ALS patients, the ALSFRS-r and characteristics of disease progression were documented (site of first paresis/atrophy, date of onset). All participants also completed validated daytime sleepiness and sleep quality questionnaires, namely the Epworth Sleepiness Scale (ESS) 65 and the Pittsburgh Sleep Quality Index (PSQI) 66 . Patients’ inclusion process The same exclusion criteria employed by Guillot SJ et al., 13 were used. The exclusion criteria were intended to exclude all possible circumstances that might otherwise alter sleep architecture. For this reason, participants who had an apnoea-hypopnea index (AHI) above 20 per hour or participants who had a periodic limb movement index (PLMSI) above 50 per hour were excluded. In particular, we intended to exclude respiratory insufficiency in ALS patients. Respiratory insufficiency develops earlier or later in the progression of ALS, depending on the individual course, but is generally present in advanced stages, and is known to influence sleep architecture. For this reason, ALS patients received transcutaneous capnometry in addition to polysomnography. Neuropsychological Assessment Cognition was measured with the German version of the Edinburgh Cognitive and Behavioural ALS Screen (ECAS) 67 – 69 by trained neuropsychologists. The ECAS addresses cognitive domains of language, verbal fluency, executive functions (ALS-specific functions) and memory and visuospatial functions (ALS non-specific functions). Age and education-adjusted cut-offs were used 69 . Behavioural changes were assessed by patient caregiver/1st-degree relative interviews on disinhibition, apathy, loss of sympathy/empathy, perseverative/stereotyped behaviour, hyperorality/altered eating behaviour and psychotic symptoms. Electroencephalography in patients and subjects All participants, ALS patients, healthy controls, fALS gene carriers and fALS controls underwent a 1-night full Polysomnography, involving monitoring of various physiological parameters including electroencephalogram (EEG), surface electromyogram (EMG), electrooculogram (EOG) respiratory effort and flow, pulse and oxygen saturation. All measurements were conducted according to the criteria of the American Academy of Sleep Medicine (AASM) guidelines 70 , 71 . The EEG electrodes were placed according to the international 10-20 system, the following electrodes were used in each subject: Fz, C3, C4, Cz, P3, P4, Pz, O1, O2, A1, and A2. The sampling rate was 512 Hz in each case. The individually different point in time at which the participant turned off the lights and tried to sleep was marked with a “lights off” marker in each recording. Sleep analyses in patients and subjects Analyses were performed using available Python packages (only compatible with Python 3.10 or newer, Python Software Foundation. Python Language Reference, version 3.12. Available at http://www.python.org ) relying on MNE package[38]. EEGs preprocessing was performed following section res2hsleep . Briefly, recordings were first de-identified using the open-source Prerau Lab EDF De-identification Tool (Version 1.0; 2023) in Python (Prerau Lab EDF De-identification Tool [Computer software], 2023, Retrieved from https://sleepeeg.org/edf-de-identification-tool ), to then be notch-filtered to remove the 50Hz powerline. Independent component analysis was performed to remove all remaining artefacts from the signal 72 – 76 . Analyses of sleep spindles, slow oscillations and K-complexes were performed on all electrodes 77 – 79 and outliers were removed using an isolation forest algorithm 80 . K-complexes analysis was limited to the sensorimotor cortex (C3), which is known to be impaired in ALS, using MNE and SciPy packages 80 , 81 . For the sleep spindle, its density (number of sleep spindles per minute of NREM 2 sleep), amplitude (peak-to-peak amplitude of the detrended sleep spindle) and root-mean-square (RMS) were analysed. For the slow oscillation, its density (number of slow oscillations per minute of NREM2), slope (slope between the negative peak and the mid-crossing of the slow oscillation), phase-amplitude coupling (PAC; slow oscillations-sleep spindles normalised PAC within a 2sec epoch centred around the negative peak of the slow oscillation) and phase at Sigma peak (slow oscillation’s phase when sigma peak is reached within a 2sec epoch centred around the negative peak of the slow oscillation) were analysed. For the K-complex assessment, we analysed its density (number of K-complexes per minute of NREM 2).Topographic maps were performed using MNE and YASA packages 82 . All analyses were performed following the AASM’s guidelines 83 . Mouse models All experiments were performed in strict compliance with Directive 2010/63/EU, and new Regulation (EU) 2019/1010, and the project was reviewed and approved by the Ethics Committee of the University of Strasbourg and the French Ministry of Higher Education, Research and Innovation (Decree n°2013-118, February 1 st , 2013). All datasets, animal care, surgery and procedures used in this study have been previously described in 13 Electrocorticography analysis in mice Data were extracted from NeuroScore ™ software for sleep and seizure analysis 3.4 (Data Science International Inc., St. Paul, MN, USA) and used in combination with already available Python packages (Python Software Foundation. Python Language Reference, version 3.12. Available at http://www.python.org ) to further process the data. Sleep spindles, slow oscillations and K-complexes were automatically detected based using publicly available pipelines 84 , 85 .The signal was first band-pass filtered at 1-45Hz, and the sigma power (12-16Hz) was calculated on a 200ms Hamming window followed by a Short-Term Fourier Transform (STFT) with the same window length. The occurrence of sleep spindles was identified when the smoothed absolute sigma power within the 12-16 Hz range exceeded 0.2 of the total power observed in the broadband frequency range of 0.1-45Hz. This signifies that a minimum of 20% of the signal’s total power must be contained within the specified sigma band. For slow oscillations, the signal was first band-pass filtered at 0.1-45Hz, and the low delta power (0.1-2Hz) was calculated on a 400ms Hamming window. Areas under the curve (AUC) were calculated using Simpson’s rule derived from the delta band (A SO ) and the total power broadband frequency range (B PSD ). The ratio of these two AUCs was then obtained, providing the slow oscillations ratio. For K-complexes, the signal was first band-pass filtered at 0.1-45Hz, and the sigma power was calculated on a 400ms Hamming window. The signal was first band-pass filtered at 1-45Hz, and the low delta power (0.3-1Hz) was calculated on a 200ms Hamming window followed by an STFT with the same window length. The occurrence of K-complexes was identified when the STFT within the 0.3-1Hz range exceeded the mean STFT of the same frequency range. Statistical analyses G*Power software (Version 3.1.9.6 for macOS; 2023) was used to determine the sufficient sample size needed to reach significant statistical power using an A priori Student’s t-test coupled with a linear bivariate regression 86 , 87 . Prior to any statistical analysis, normality and homoscedasticity were both tested respectively with Shapiro-Wilk test 88 and Bartlett’s test 89 . Statistical analysis of two groups was performed using an independent Student’s t-test, from Pingouin 90 , using the Welch t-test correction, from SciPy, as recommended by Zimmerman 91 and with a large Cauchy scale factor due to the considerate effect size 92 . When data were heteroscedastic and normality was not met, a Mann-Withney U test was performed using SciPy 80 . Follow-up analysis were performed using paired t-test from SciPy 80 or a Wilcoxon-Mann-Whitney rank-sum test from statsmodels 93 when normality was not met. P values were then adjusted using FDR-BKY correction. For statistical analysis of three or four groups, a One-way ANOVA or Two-way ANOVA was performed using Pingouin 90 toolbox. For both One-way ANOVA and Two-way ANOVA, a one-step Bonferroni correction was applied. When data were heteroscedastic and normality was not met, a Kruskal-Wallis from SciPy 80 followed by Dunn’s multiple comparison test with FDR-BKY correction was performed using scikit-posthocs 94 , instead of a One-Way ANOVA. For the Two-Way ANOVA, a generalized least squares model was fitted using statsmodels 93 , followed by Dunn’s multiple comparison and FDR-BKY correction using scikit-posthocs 94 . We evaluated whether a sex-specific effect was present in all our analyses by performing a Two-way ANOVA with a one-step Bonferroni correction for both sexes. Sex was self-reported in both ALS cohorts. Spearman’s correlation coefficient from SciPy 80 , were used to determine correlations on non-parametric data. Data are presented as violin plots with all points and expressed as average ± interquartile. Plots were generated using Seaborn and Matplotlib packages 95 . Results were deemed significant when their adj. p value <0.05. Here, only corrected p values (adj. p value ) are shown. Funding This work was funded by Agence Nationale de la Recherche (ANR-19-CE17-0016, ANR-20-CE17-0008, ANR-24-CE37-4064 to LD), by the Interdisciplinary Thematic Institute NeuroStra, as part of the ITI 2021-2028 (Idex Unistra ANR-10-IDEX-0002, ANR-20-SFRI-0012), by Fondation Bettencourt (Coup d’élan 2019 to LD), Fondation pour la recherche médicale (FRM, DEQ20180339179), Axa Research Funds (rare diseases award 2019, to LD), Fondation Thierry Latran (HypmotALS to LD and FR, Trials to FR), Association Francaise de Recherche sur la sclérose latérale amyotrophique (2024 to LD), Radala Foundation for ALS Research (to LD and FR), the Association Française contre les Myopathies (AFM-Téléthon, #23646 and #28944 to LD), TargetALS (to FR and LD) and JPND (HiCALS project, to FR and LD). LD is USIAS fellow 2019. Fondation Anne-Marie et Roger Dreyfus (hosted by Fondation de France) provided salary for SJG. CL was supported by a salary from the Charcot Stiftung. 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