APOE-ε4Modulates Facial Neuromuscular Activity in Nondemented Adults: Toward Sensitive Speech-Based Diagnostics for Alzheimer’s Disease

APOE-ε4 Modulates Facial Neuromuscular Activity in Nondemented Adults: Toward Sensitive Speech-Based Diagnostics for Alzheimer’s Disease | 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 APOE- ε4 Modulates Facial Neuromuscular Activity in Nondemented Adults: Toward Sensitive Speech-Based Diagnostics for Alzheimer’s Disease Marziye Eshghi , Panying Rong , Mehrdad Dadgostar , Hyunmin Shin , Brian D. Richburg , Nelson V. Barnett , David H. Salat , Steven E. Arnold , Jordan R. Green doi: https://doi.org/10.1101/2025.04.29.25326665 Marziye Eshghi 1 MGH Institute of Health Professions , 1 Constitution Wharf, Charlestown, MA, 02129, USA 2 Athinoula A. Martinos Center for Biomedical Imaging, Harvard-MIT Health Sciences and Technology School, Massachusetts General Hospital , 149 13th St, Charlestown, MA, 02129, USA 3 Massachusetts General Hospital, Department of Radiology, Harvard Medical School , 55 Fruit St., Boston, MA, 02114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: meshghi{at}mgh.harvard.edu Panying Rong 4 University of Kansas, Department of Speech-Language-Hearing: Science and Disorders , 1000 Sunnyside Ave., Lawrence, KS 66045, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mehrdad Dadgostar 1 MGH Institute of Health Professions , 1 Constitution Wharf, Charlestown, MA, 02129, USA 2 Athinoula A. Martinos Center for Biomedical Imaging, Harvard-MIT Health Sciences and Technology School, Massachusetts General Hospital , 149 13th St, Charlestown, MA, 02129, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hyunmin Shin 1 MGH Institute of Health Professions , 1 Constitution Wharf, Charlestown, MA, 02129, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brian D. Richburg 1 MGH Institute of Health Professions , 1 Constitution Wharf, Charlestown, MA, 02129, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nelson V. Barnett 1 MGH Institute of Health Professions , 1 Constitution Wharf, Charlestown, MA, 02129, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site David H. Salat 2 Athinoula A. Martinos Center for Biomedical Imaging, Harvard-MIT Health Sciences and Technology School, Massachusetts General Hospital , 149 13th St, Charlestown, MA, 02129, USA 3 Massachusetts General Hospital, Department of Radiology, Harvard Medical School , 55 Fruit St., Boston, MA, 02114, USA 5 Neuroimaging Research for Veterans Center , VA Boston Healthcare System, 150 S Huntington Ave, Boston, MA 02130, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steven E. Arnold 2 Athinoula A. Martinos Center for Biomedical Imaging, Harvard-MIT Health Sciences and Technology School, Massachusetts General Hospital , 149 13th St, Charlestown, MA, 02129, USA 6 Massachusetts General Hospital, Department of Neurology, Harvard Medical School , 55 Fruit St., Boston, MA, 02114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jordan R. Green 1 MGH Institute of Health Professions , 1 Constitution Wharf, Charlestown, MA, 02129, USA 7 Speech and Hearing Bioscience and Technology, Harvard University , 77 Massach us etts Ave., Building E25-518, Cambridge, MA 02139 USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Preview PDF Abstract The APOE-ε4 allele is a genetic risk factor for late-onset Alzheimer’s disease (AD). Beyond cognitive decline, APOE-ε4 affects motor function, reducing muscle strength and coordination, potentially through mitochondrial dysfunction and oxidative stress. This study examined the influence of the APOE- ε4 allele on neuromuscular function in oral muscles involved in speech production, using surface electromyography (EMG); and assessed the predictive power of EMG measures in differentiating APOE- ε4 carriers from noncarriers. Forty-two cognitively intact adults (16 APOE- ε4 carriers, 26 noncarriers) completed speech tasks while EMG was recorded from seven craniofacial muscles. Seventy EMG features including amplitude, frequency, complexity, regularity, and functional connectivity were extracted. Statistical analyses assessed genotype effects, sex differences, and correlations with blood metabolic biomarkers. APOE- ε4 carriers exhibited increased motor unit recruitment and synchronization, suggesting accelerated muscle fatigue. EMG-based measures outperformed cognitive tests in distinguishing carriers (AUC = 0.90) and correlated with metabolic biomarkers. Sex differences emerged, with female carriers showing reduced and male carriers showing increased functional connectivity. These findings highlight speech-based neuromuscular changes as potential early biomarkers of Alzheimer’s risk before cognition is affected. 1. Introduction The Apolipoprotein E4 (APOE -ε4 ) allele is a well-established genetic risk factor for late-onset Alzheimer’s disease (AD), influencing disease progression and pathophysiology. 1 , 2 This allele influences several biological markers of AD, including amyloid beta (Aβ) accumulation, cerebral glucose hypometabolism, cortical atrophy, and vascular abnormalities. 3 – 15 Furthermore, APOE -ε4 exacerbates tau pathology, neuroinflammation, and gliosis in both human AD brains and mouse models. 16 – 21 Carriers of APOE- ε4 face a significantly higher risk of developing mild cognitive impairment (MCI) and AD compared to non-carriers, with the presence of this allele potentially accelerating cognitive decline and leading to earlier onset of symptoms. 13 , 22 – 28 Additionally, emerging research indicates that the APOE -ε4 allele may significantly impact motor performance and neuromuscular function, leading to measurable changes in muscle strength, motor coordination, and overall physical performance. 29 – 36 These effects suggest a broader influence of APOE -ε4 beyond cognitive decline and AD risk, highlighting its potential role in neuromuscular degeneration and motor system vulnerabilities. Research has shown that individuals carrying at least one copy of the ε4 allele experience a faster rate of motor decline in older age, with a two-fold increase compared to non-carriers. 29 This accelerated decline is largely attributed to changes in muscle strength, suggesting that APOE- ε4 may influence neuromuscular function through its effects on muscular performance. 29 APOE- ε4 effects on motor function can be linked to APOE- ε4 -mediated mechanisms such as mitochondrial dysfunction, oxidative stress, and disrupted synaptic signaling, all of which can impair motor performance and contribute to accelerated motor decline. 37 While gross motor tasks primarily involve larger muscle groups and may overlook subtle deficits, speech production, which relies on highly coordinated, continuous activation of fine motor units, presents a unique opportunity to detect early motor impairments in APOE-ε4 carriers—potentially preceding overt cognitive or physical symptoms. Converging evidence shows that measurable subclinical changes in speech-related muscle activities can be detected early in neurodegeneration. 38 – 40 This study aims to investigate the influence of the APOE- ε4 allele on neuromuscular function in oral muscles involved in speech production, using surface electromyography (EMG) to capture muscle activity. The primary objective is to evaluate the predictive power of EMG measures extracted from craniofacial muscles during speech, comparing their accuracy in classifying APOE- ε4 carriers and noncarriers with commonly used measures obtained from cognitive testing. To understand the pathological mechanisms through which APOE- ε4 impacts neuromuscular function, this study will further examine the associations between EMG measures and blood-based lipid and metabolic markers. These biomarkers will assess mitochondrial function, oxidative stress, and metabolic activity, shedding light on how these factors may influence orofacial neuromuscular functions in APOE- ε4 carriers. Given the well-documented disruptive effects of APOE- ε4 on mitochondrial pathways—causing oxidative stress, impaired ATP synthesis, and motor decline 29 , 37 , 41 – 43 —understanding the relationship between APOE- ε4 pathology and neuromuscular dysfunction may provide novel insights into the early identification and monitoring of risk in APOE- ε4 carriers. We hypothesize that carrying the APOE- ε4 allele contributes to motor unit remodeling or loss, resulting in reduced neuromuscular efficiency. These alterations could manifest as atypical motor unit recruitment and firing patterns during speech tasks, potentially reflected by increased complexity, greater variability, or the adoption of compensatory strategies. Such neuromuscular changes may be characterized by modified EMG signal amplitudes and frequency shifts, indicating altered conduction velocities and disrupted motor unit synchronization. Furthermore, the APOE- ε4 allele may influence central motor pathways, affecting the functional connectivity between neural structures—such as brain-to-muscle interactions or coordination between functionally related muscle groups. These effects can be assessed using coherence measures obtained from EMG-EEG or EMG-EMG signal analyses. A reduction in coherence may suggest impaired neural signal integration during motor tasks. The neuromuscular alterations associated with APOE- ε4 may stem from chronic metabolic impairments, contributing to motor unit remodeling, diminished neuromuscular efficiency, and accelerated muscle fatigue. By investigating EMG patterns in speech-related muscles, this study aims to elucidate the impact of APOE- ε4 on neuromuscular function, potentially identifying biomarkers useful for early diagnosis, disease monitoring, and the development of personalized therapeutic interventions. 2. Methods The study was approved by the Mass General Brigham (MGB) Institutional Review Board (IRB Protocol #2021P001460). All procedures were conducted in accordance with MGB IRB guidelines and regulations. Written informed consent was obtained from all participants prior to their participation. 2.1 Participant population Forty-two adults participated in this study. Participants were divided into two groups based on their APOE genotype: APOE -ε4 positive (ε4+) (n=16) and APOE -ε4 negative (ε4-) (n=26). The ε4+ group included individuals with ε3/ε4 heterozygous and ε4/ε4 homozygous genotypes, while the ε4-group consisted of individuals with the ε3/ε3 homozygous genotype. The groups were matched for age, and education (>12 years). APOE genotyping was conducted using blood samples, and all participants were blind to their respective genotypes. Of the 42 participants, 28 (7/5 female/male in the ε4+ group; 4/12 female/male in the ε4-group) had available blood-based lipid, metabolic, hematological, and vitamin/mineral markers, from which a subset was selected based on their relevance to mitochondrial function, metabolism, and oxidative stress. These data, along with demographic and clinical information for both groups are summarized in Table 1 . View this table: View inline View popup Download powerpoint Table 1. Demographic and clinical characteristics stratified by genotype carrier status. All participants met the following inclusion criteria: (1) capacity to provide informed consent and comply with task-specific study instructions; (2) native speakers of American English; (3) no previous record of speech, language, hearing, psychiatric, or neurological disorders; (4) no clinical diagnosis of MCI; (5) not currently using psychoactive drugs that could potentially impact study outcomes; (6) passed an age-adjusted pure-tone hearing screening, ensuring normal hearing acuity in at least one ear; and (7) dental and occlusion status within functional limits, as determined by an oral examination performed by ME. Additionally, all participants demonstrated normal functional speech as assessed through the Sentence Intelligibility Test (SIT), a standardized tool that measures speech intelligibility and speaking rate. During the assessment, participants read 11 sentences randomly generated by the SIT software, with sentence length progressively increasing from 5 to 15 words. Their speech was digitally recorded and subsequently transcribed orthographically by two native American English-speaking listeners with established high interrater reliability (ICC= 0.99, p<0.001). The SIT software calculated speech intelligibility as the percentage of correctly transcribed words and speaking rate as the number of words read per minute (WPM). Furthermore, participants exhibited no sound distortions or abnormalities in voice, resonance, prosody, or rate qualities, as independently judged by two expert raters (ME and PR). 2.2 Experimental tasks Participants were presented with nine sentences and instructed to read each sentence a fixed number of times. They produced five repetitions of the sentence “Buy Bobby a puppy” and two repetitions of each of the remaining eight sentences. These sentences were: “Billy blows big blue bubbles”; “Busy buzzing bumble bees”; “Bake big batches of bitter brown bread”; “Bankers pull plenty of bills into banks”; “Betty picked plenty of apple bits from the baby’s bib”; “Blue balloons pop when placed above sharp spikes”; “Ben’s brother, Patrick, beat up Ben’s pal, Bill”; and “Paul baked plenty of baguettes but put butter on the bottom.” The sentences were selected because they include speech sounds that engage seven targeted speech muscles, including two lip muscles (inferior and superior orbicularis oris of the lip and five jaw muscles (left and right masseter, left and right temporalis, and anterior belly of the digastric (ABD)). The sentences are particularly rich in bilabial consonants (/b/, /p/, /m/), which require substantial activation of the orbicularis oris muscles. For instance, “Buy Bobby a Puppy” and “Billy blows big blue bubbles” prominently feature these sounds, making them ideal for observing the coordinated action of facial muscles. Similarly, the plosives (/b/, /p/) and fricatives (/f/, /v/) in phrases “Bake big batches” and “Blue balloons pop” engage the orbicularis oris, ABD and other facial muscles in rapid and precise movements. Alveolar sounds (/t/, /d/, /n/) in words like “Betty,” “plenty,” and “bottom” involve significant activation of the temporalis and masseter muscles for jaw positioning and stabilization. These sounds also recruit the ABD muscles for jaw opening, particularly in transitions between consonants and vowels. Phrases like “plenty” and “spikes” include consonant clusters that demand complex coordination across articulatory muscles, offering further insights into dynamic muscle control. The phonetic structure of sentences such as “Busy buzzing bumble bees” promotes repeated activation of the targeted craniofacial muscle groups. Additionally, the sentences feature a diverse range of vowel sounds, requiring varied configurations of the oral cavity and corresponding activation patterns of the speech muscles, including the ABD for jaw movement during low and mid vowels. 44 While phonetically rich, these sentences also closely resemble natural speech, eliciting more realistic muscle activation patterns than those produced during isolated phoneme tasks. In summary, the combination of diverse consonant types and vowel contexts generates a comprehensive range of muscle activation patterns, making these stimuli particularly effective for EMG analysis of speech production. 2.3 Neuropsychological Assessment Participants underwent a comprehensive array of standard neuropsychological tests routinely used to characterize AD-related conditions which assess attention, processing speed, learning, memory, language, and executive function. Cognitive tests used in this study included Montreal Cognitive Assessment (MoCA), Mini-Mental Status Examination (MMSE), Trail Making Tests A and B, the Hopkins Verbal Learning Test (HVLT), and the Controlled Oral Word Association Test (COWAT). The MoCA is a widely used cognitive screening tool designed to evaluate a broad range of cognitive abilities and is particularly sensitive to detecting MCI and early-stage dementia. 45 – 50 It provides a comprehensive assessment across multiple cognitive domains, including attention and concentration, executive functioning, memory, language, visuospatial abilities, orientation, and abstract thinking. Attention and concentration are assessed through digit spans, vigilance, and serial subtraction, while executive functioning is evaluated using tasks such as trail-making exercises and abstraction questions. Memory is measured through short-term recall of a word list, reflecting encoding and retrieval processes. Language is assessed through naming, sentence repetition, and verbal fluency tasks, while visuospatial abilities are tested using clock drawing and cube copying exercises. Orientation is evaluated through questions about the date, time, and place, and abstract thinking is measured by identifying similarities between items. The MoCA provides a total score out of 30, with lower scores indicating potential cognitive impairment. A score below the cutoff (typically 26) suggests the need for further assessment or intervention. The MMSE is a widely used cognitive screening tool designed to assess global cognitive function and detect potential cognitive impairment. 51 – 57 This brief, standardized test evaluates various cognitive domains, providing an overall measure of cognitive abilities. The MMSE assesses orientation by asking individuals to identify the date, day of the week, and their current location, reflecting awareness of time and place. Registration is tested by asking participants to repeat a list of three unrelated words, while attention and calculation are evaluated through tasks such as serial sevens (subtracting 7 repeatedly from 100) or spelling a word backward, measuring concentration and working memory. Delayed recall of the three words presented earlier assesses memory. Language abilities are tested through tasks like naming common objects, repeating sentences, following instructions, and writing a sentence, evaluating expressive and receptive language skills. Visuospatial skills are assessed by asking participants to copy a simple geometric figure, measuring visual perception and motor coordination. The MMSE provides a total score out of 30 points, with lower scores indicating greater cognitive impairment. A score of 24 or below is often used as a cutoff to indicate cognitive deficits, though adjustments may be made based on age, education, and cultural factors. The TMT , comprising two parts (A and B), is a widely used neuropsychological assessment designed to evaluate various cognitive abilities, including visual attention, task switching, processing speed, and executive functioning. 58 – 64 In Part A, participants connect a sequence of randomly arranged numbers in ascending order by drawing a line as quickly and accurately as possible. This segment primarily measures visual scanning, numerical sequencing, and motor speed. Part B requires participants to alternate between different cognitive tasks or mental sets, assessing cognitive flexibility, divided attention, and the inhibition of impulsive responses, while also incorporating the skills evaluated in Part A. 65 – 69 Longer completion times and increased errors on the TMT are indicative of cognitive impairments and can affect everyday activities, including driving. Although the TMT is often scored using a ratio of Part B to Part A or by adjusting Part B scores relative to Part A, this analysis focused on evaluating each part independently. This approach allows for a more comprehensive exploration of distinct cognitive demands, encompassing both higher-order and lower-order processes. The HVLT is a widely used measure of verbal learning and memory, consisting of multiple subtests that assess various aspects of cognitive functioning. 70 – 73 These include immediate recall, delayed recall, and retention efficiency, which provide insights into encoding, storage, and retrieval processes in memory. The HVLT Immediate Total Recall subtest evaluates a participant’s ability to immediately recall a list of words presented verbally over multiple learning trials. It reflects short-term memory capacity, attention, and the initial encoding of verbal information. Performance on this measure is influenced by the ability to organize and retain information during the learning process. The HVLT Delayed Recall component assesses the participant’s ability to recall the same word list after a delay, without re-presentation of the list. It provides information on the strength of memory retention over time and the ability to retrieve information from long-term memory. Lower performance can indicate challenges with memory storage or retrieval. Finally, the HVLT Percent Retention measures the proportion of words recalled during the delayed recall phase relative to the words initially learned (immediate recall). It serves as an indicator of memory decay and retention efficiency. Higher retention percentages suggest more effective memory consolidation, while lower percentages may highlight difficulties in retaining learned information over time. These HVLT subtests, when analyzed together, offer a comprehensive understanding of verbal memory performance, enabling the identification of patterns associated with encoding, retention, and retrieval deficits. The COWAT , specifically the FAS variant, is a widely used measure of phonemic verbal fluency designed to assess language production and executive functioning. 74 – 86 This test examines the participant’s ability to generate words under specific phonological constraints, providing insights into cognitive processes such as mental flexibility, retrieval strategies, and inhibitory control. In this task, participants are asked to produce as many words as possible within a given time frame (typically 60 seconds per letter) that begin with a specified letter—F, A, or S—excluding proper nouns (i.e., name of people of places), numbers, and derivatives of the same word (i.e., repetitions of the same word with different affixes). Performance on this test evaluates the ability to organize and retrieve words based on phonemic rules while suppressing irrelevant or automatic responses. The FAS test relies on multiple cognitive domains, including language skills (retrieval of vocabulary and phonological processing), executive functioning (strategic planning, mental flexibility, and switching between subcategories within the letter constraint), and inhibitory control (suppressing inappropriate responses that do not meet the criteria). Reduced word generation or clustering, such as producing words only within a narrow subcategory, may indicate impairments in cognitive flexibility, processing speed, or access to lexical stores. Performance on the FAS test is commonly used in neuropsychological evaluations to identify deficits associated with frontal lobe dysfunction, neurological conditions, or disorders that impact executive functioning and language production. All tests were scored using standardized norms adjusted for demographic variables including age, sex, education level, and ethnicity when appropriate to calculate the z-scores for each test. Subsequently, the cognitive composite z-score for each participant was calculated by summing the six standardized performance scores (i.e. TMT A, TMT B, HVLT-Immediate Total Recall, HVLT Delayed Recall, HVLT Percent Retention, COWAT-FAS). To evaluate the clinical diagnosis of MCI, participants’ cognitive status was assessed using five diagnostic frameworks: Conservative Subtype, Custom Criteria, Custom Subtype, Petersen Criteria, and Petersen Subtype. Approximately 60% of subjects demonstrated normal cognitive status across all criteria and subtypes, while 40% exhibited abnormal cognition (e.g., MCI, single amnestic) on two of these. However, all participants were ultimately classified as cognitively intact using a conservative approach, as they met the criteria for normal cognition in at least three of the five diagnostic frameworks. This method prioritizes higher specificity in identifying truly cognitively normal individuals. All neuropsychological data were administered, scored, and interpreted by an experienced neuropsychologist. Administration of neuropsychological batteries and EMG recordings took place either on the same day (for 12 participants) or on different days (for 30 participants), depending on the participant’s availability and preference. For sessions conducted on separate days, the interval was on average three months to maintain temporal proximity between cognitive and EMG data. 2.4 EMG Recording EMG data collection was conducted in the Speech Physiology and Neurobiology of Aging and Dementia (SPaN-AD) lab at MGH Institute of Health Professions. Participants were seated in a chair, with speech tasks presented in a quasi-random order on a screen approximately 6 feet away. Each participant was fitted with surface electrodes for myoelectric data acquisition and a head-mounted microphone for acoustic data collection as described below. EMG data were collected bilaterally from multiple facial and jaw muscles using a wireless EMG system (BIOPAC). Self-adhesive bipolar Ag/AgCl electrodes (15 mm diameter, 20 mm inter-electrode distance) were placed on the upper lip (two electrodes), lower lip (two electrodes), anterior belly of digastric (ABD, two electrodes), masseter (four electrodes, two pairs on each side), and temporalis (four electrodes, two pairs on each side). A ground electrode was attached behind the ear over the mastoid process. Prior to electrode placement, the skin was prepared using an alcohol swab to increase conductance. Electrodes were positioned using craniofacial anatomical landmarks for reproducibility: along the cantho-gonial line for masseter, vertically at the coronal suture for anterior temporalis, and submentally along the posterior-inferior direction for ABD. Electrode placement was verified through calibration tasks such as jaw oscillations and clenching to ensure proper capture of muscle activities. The EMG signals were pre-amplified by 1,000, band-pass filtered at 10-500 Hz, digitized at 10,000 Hz using the MP150 data acquisition module, and recorded using AcqKnowledge software (BIOPAC Systems, Inc.). Acoustic data used to annotate the EMG recordings were acquired using a Williams Sound MIC094 head-mounted microphone placed approximately 5 cm from the left lip corner. The audio signal was processed through a Focusrite Scarlett 2i2 audio interface and digitized at 10,000 Hz, synchronously with the EMG data. Participants were instructed to read sentences at their habitual speaking rate and loudness. The simultaneous recording of myoelectric and acoustic data allowed for temporal alignment during subsequent analysis. 2.5 EMG data processing and analysis All EMG data processing and analytic procedures were implemented in MATLAB 87 using custom-developed scripts. Following the best-practice guidelines for EMG signal processing, 88 , 89 all seven channels were notch-filtered at 60 Hz and high-pass filtered at 20 Hz to remove power line noise and low-frequency motion artifacts, respectively. Lastly, the EMG signals for all repetitions of each stimulus were concatenated, resulting in 375 data samples (42 participants × 9 stimuli – 3 errors = 375) in total. The processed EMG data samples were analyzed using an integrative automated analytic approach that adopted and combined two recently developed and validated EMG analytic algorithms, originally designed for a different neurodegenerative population. 38 , 90 This method extracts 70 features across the seven EMG channels. An overview of these features is outlined in Table 2 . This analytic approach combined linear and nonlinear analyses in time and frequency domains to measure the frequency, amplitude, complexity, and regularity of individual muscle activities, as well as the functional connectivity of the whole craniofacial muscle network. View this table: View inline View popup Download powerpoint Table 2. Overview of surface electromyography features. Technical details about these analyses are described below. 2.5.1 ​Frequency-domain analysis The frequency-domain analysis calculated the power spectral density of each EMG signal and the coherence spectra between different EMG signals. Power spectral density was estimated by Welch’s method, which was implemented by 1024-point Fast Fourier Transform (FFT) applied over a sliding 100-msec, 50% overlap Hamming window. 91 The median frequency (MDF) of power spectral density was calculated as the frequency that divided the power spectrum into two parts with equal integrated spectral energy. To calculate coherence spectra, all EMG signals were full wave rectified and reconstructed by concatenating stationary 1-sec epochs around the bursts for each channel. Magnitude-squared coherence spectrum was calculated for each pair of reconstructed signals, using 8192-point FFT applied over a sliding 8192-point Hamming window with 75% overlap, based on the Eq. 1 : where |R xy | 2 is magnitude-squared coherence spectrum, S xy (f) is the cross-spectrum between two muscles, and S xx (f) and S yy (f) are the auto-spectra for each muscle. In addition, a significance level corresponding to the upper 95% confidence limit under the hypothesis of independence between muscles was calculated as: S = 1 − 0.05 1/( L^ −1) , where L ^ is the adjusted number of overlapped segments in coherence calculation. 92 , 93 The significance level was used to exclude spurious functional connections between muscles. In this study, we focused on the coherence in the beta band. Beta oscillations (15-35 Hz) originate from the motor cortical network, providing a descending drive for synchronous modulation of functionally related muscles. 94 – 96 Thus, beta-band coherence can be interpreted as a measure of functional connectivity between muscles related to a common cortical drive. To measure beta-band coherence, the significance of the coherence spectrum within the beta band was first verified. Specifically, if no peak in the beta band of the coherence spectrum exceeded the significance level, the functional connection between the muscles was regarded as spurious, and beta-band coherence was set to zero; otherwise, the mean coherence within the beta band was calculated. After the verification of significance, all beta-band coherence metrics were transformed to Fisher z-scores for variance stabilization. 92 Lastly, the mean strength of functional connectivity was calculated for each muscle as the average sum of Fisher z-score of beta-band coherence between this and all other muscles. 2.5.2 ​Time-domain analysis The time-domain analysis was applied to 100-msec segments centered at the bursts, and the features extracted were averaged across all segments for each channel. Such short segments represent the initial phase of muscle contraction, which encodes the most stable and useful neuromuscular information. 97 This analysis extracted five features for each muscle, including: Mean absolute value (MAV), defined as the mean absolute value of all data points within a segment: where N is the number of data points in the segment and x i is i th data point in the segment. Mean absolute value slope (MAVSlp), defined as the difference of MAV between adjacent segments: where j = 1,2, …, J − 1 and J is the total number of segments in the EMG signal. Zero crossings (ZR), defined as the number of times the waveform crosses zero: where . To eliminate noise fluctuations, a 4 mV threshold (i.e., 4 μ V peak-to-peak intrinsic system noise × 2000 gain/2) is implemented while calculating ZR. Slope sign change (SlpSignChange), defined as the number of times the slope of the waveform changes sign: Like for ZR, a 4 mV threshold is employed to reduce noise-induced slope sign changes. Waveform length (WL), defined as the cumulative length of the waveform over the segment: 2.5.3 ​Time-frequency domain analysis First, each EMG signal was decomposed into eight scale-frequency representations, using 3-level wavelet packet decomposition with the 5 th order Symlets wavelet. 98 Next, the Shannon entropy of each time-frequency representation was computed as: where s i is the i th coefficient of the scale-frequency representation, reflecting the approximation (i.e., low-pass) or detail (i.e., high-pass) coefficients at each decomposition level. Lastly, the sum of the Shannon entropy of all eight scale-frequency representations was calculated to index the irregularity of myoelectric activity for each muscle. 2.5.4 ​Recurrence Quantification Analysis (RQA) RQA quantified the structural complexity of EMG signals based on the dynamics of state change in the recurrence plot. 99 , 100 A recurrence plot is composed of recurrence points, where a recurrence point is defined as a state j when its phase space trajectory x⃗ j returns to a previous state i so that the distance between x⃗ j and x⃗ i is below the threshold ε. Here the phase space trajectory x⃗ j is defined as: x⃗ j = [ u j , u j +τ , … u j +( m −1)τ ], where m is the embedding dimension parameter, and τ is the time delay parameter. Recurrent plots were generated for non-overlapping 1-sec segments of each EMG signal, with the parameters set to the following pre-determined values: m = 30, τ = 3, ε = 0.2. Such segments represented stationary intervals of EMG signals, allowing for reliable analysis of the underlying motor unit firing patterns. Two quantitative metrics—recurrence rate (RR) and determinism (DET)—were derived from each segment and then averaged across all segments for each muscle. RR measured the ratio of all recurrence points to all possible states in the recurrence plot. DET measured the ratio of recurrence points forming diagonal structures to all recurrence points, which can be interpreted as an index of the overall periodic content in the signal. 2.6 Statistical analysis All statistical analyses were conducted in the R statistical computing program, 101 with the significance level set to p < .05. Prior to the analyses, all EMG features were subject to a data cleaning and imputation procedure, by (1) identifying and excluding all outliers outside the range of [ lower quantile – 1.5* IQR, upper quantile + 1.5*IQR ] and (2) imputing missing data using the k-nearest neighbors (KNN) algorithm ( preProcess ). 102 The imputed data were submitted to statistical analyses as described below. 2.6.1 ​Effect of APOE-ε4 on neuromuscular function To provide an initial inspection of the effect of APOE -ε4 on neuromuscular function, the effect size of the difference between all EMG features for APOE -ε4 carriers and noncarriers was calculated using Cohen’s d. Due to the differential impact of APOE -ε4 on males and females, 103 – 107 the effect sizes were calculated for the two sexes separately. 2.6.2 ​Factor analysis To reduce the dimension of the EMG feature set, all features were submitted to maximum likelihood factor analysis ( factanal ) with oblimin rotation to cluster features that shared common variance into a lower-dimensional set of latent factors, where the number of factors was determined by parallel analysis ( fa.parallel ). 108 This procedure aimed to circumvent the curse of dimensionality, as commonly encountered in digital health applications, 109 to avoid overfitting to a specific dataset in the subsequent classification analysis. The resulting latent factors represented unobserved neuromuscular traits of the craniofacial muscle network. Factor scores were estimated using the tenBerge method, providing EMG-based measures of participants’ neuromuscular performance in the latent factor space. 2.6.3 ​Predictive power of EMG-based measures for classifying APOE-ε4 carriers from noncarriers To evaluate the discriminatory power of each EMG-based measure for differentiating APOE -ε4 carriers from noncarriers, each EMG-based measure was submitted to a linear mixed effects model, with genotype (i.e., binary variable denoting APOE -ε4 carriers versus noncarriers), sex, age, and the interaction between genotype and sex as the fixed effects, and a participant-dependent intercept term as the random effect. The main effect of APOE -ε4 and its interaction with sex were the primary effects of interest for evaluating the significance of carrier-noncarrier difference. Next, all EMG-based measures were aggregated by averaging across the nine stimuli for each participant. The Receiver Operating Characteristic (ROC) statistic was calculated for each aggregated measure to index its discriminatory power for differentiating carriers from noncarriers. To compare the discriminatory power of the EMG-based measures with standard measures derived from the cognitive domain (i.e., scores on MoCA,MMSE, and the comprehensive z-score of all domain-specific neuropsychological tests), all cognitive measures underwent a similar analysis as above. Specifically, general linear models were used to evaluate the effects of genotype, sex, age, and the interaction between genotype and sex on each cognitive measure. Furthermore, the ROC statistic was calculated for all cognitive measures. In addition to the discriminatory power of individual measures, we also evaluated the efficacy of the combination of all EMG-based measures for differentiating APOE -ε4 carriers from noncarriers. To this end, all EMG-based measures, along with age and sex, were fed into penalized logistic regression with the elastic net penalty to classify between the samples for carriers and noncarriers. The penalty was imposed to reduce the coefficients of minimally contributing predictors toward zero, allowing the coefficients to inform the relative importance of the predictors. The performance of classification was estimated by the area under the curve (AUC), accuracy, sensitivity, and specificity, through 5-fold cross-validation repeated 10 times. 2.6.4 ​Correlation of EMG-based measures with blood-based biomarkers Pearson’s correlation coefficient was calculated between all EMG-based measures and blood-based biomarkers. The magnitude of the correlation coefficients was interpreted to evaluate the relation between the biolological and neuromuscular factors, in order to identify potential biological underpinnings of the APOE -ε4 -induced neuromuscular changes. 3. Results 3.1 Effect of APOE-ε4 on EMG features The effect size of the difference between the EMG features for APOE- ε4 carriers and noncarriers is displayed by sex in Figure 1 . Based on these results, the APOE- ε4 allele showed both common and distinct effects on neuromuscular function for males and females. Download figure Open in new tab Figure 1. Cohen’s d effect size of the difference between the surface electromyograph features for (a) male APOE-ε4 carriers and noncarriers; (b) female APOE-ε4 carriers and noncarriers. RMAS = right masseter, LMAS = left masseter, RTEMP = right temporalis, LTEMP = left temporalis, ABD = anterior belly of digastric, UL = upper lip muscles, LL = lower lip muscles. Focusing on the features that exhibited at least a medium effect size (|d| > 0.50), the common effects of APOE- ε4 for both sexes included (1) increased amplitude (i.e., increased MAV and WL), especially for lip muscles, (2) increased frequency (i.e., increased MDF, ZR, SlpSignChange), (3) increased complexity (i.e., decreased RR and DET), and (4) decreased regularity (i.e., increased ShanEn). The distinct effects of APOE- ε4 for males and females were primarily manifested in functional connectivity. Specifically, male APOE- ε4 carriers exhibited a trend toward increased functional connectivity (i.e., increased str), whereas female APOE- ε4 carriers showed an opposite trend toward reduced functional connectivity (i.e., decreased str), relative to their noncarrier counterparts. 3.2 Factor analysis The 70 EMG features were clustered into 14 latent factors. The resulting factorization model accounted for 64.1% of the total variance in the original feature set and was determined to be sufficient based on χ 2 test (χ 2 = 4298.64, p < .001). The rotated factor loadings are shown in Figure 2 . Download figure Open in new tab Figure 2. Rotated factor loadings. The height of the bars represents the loading of the surface electromyography features on each latent factor. By convention, features that load greater than 0.4 (i.e., absolute value > 0.4) are regarded as the primary component features of the latent factor, and the loadings of these features are shown in red, while the loadings of the other features are shown in blue. Features that loaded greater than 0.4 (i.e., absolute factor loading > 0.4) were conventionally regarded as the primary component features of the factor. Accordingly, the primary component features of factors 1 through 14 are identified and listed in Table 3 . Based on these feature clusters, all 14 latent factors were interpretable, reflecting either muscle-specific (i.e., factors 1, 2, 3, 6, 8, 13, 14) or construct-specific (i.e., factors 4, 5, 7, 9, 10, 11, 12) characteristics. View this table: View inline View popup Download powerpoint Table 3. Results of factor analysis. 3.3 Discriminatory power of EMG-based measures for classifying APOE-ε4 carriers from noncarriers Of all 14 EMG-based measures, sex showed a significant effect on one muscle-specific measure (i.e., X6, reflecting ABD characteristics), F (1,37.01) = 5.08, p = .027. Genotype, age, and the interaction between genotype and sex did not have a significant effect on any of the measures. Figure 3 shows the box plots for all EMG-based measures by genotype and sex. The ROC statistics, including AUC and the sensitivity and specificity at the optimal cutoff that maximizes the sum of sensitivity and specificity, are provided in Table 4 . Download figure Open in new tab Figure 3. Box plots for EMG-based measures (X1 – X14) by genotype (wE4: APOE-ε4 carriers; woE4: APOE-ε4 noncarriers) and biological sex. View this table: View inline View popup Download powerpoint Table 4. Area under the curve (AUC), sensitivity, and specificity of all EMG-based measures (X1 – X14) and cognitive measures (CompCog: composite z-score of all domain-specific neuropsychological tests; MoCA: z-score on the Montreal Cognitive Assessment; MMSE: z-score on the Mini-Mental State Exam) for differentiating APOE- ε4 carriers from noncarriers. The last column shows the coefficients of penalized logistic regression, reflecting the relative importance of the EMG-based measures to the classification model between carriers and noncarriers. Of the three standard cognitive measures, no significant effect was found for genotype, sex, or the interaction between genotype and sex, whereas age showed a significant effect on the z-score of MMSE, F (1,37) = 14.32, p < .001. The ROC statistics for all cognitive measures are displayed alongside the EMG-based measures in Table 4 . Based on the comparisons of the ROC statistics, the best-performing EMG-based and cognitive measures were identified, which were X1 and the z-score of MMSE, respectively, and their ROC curves are shown in Figure 4 . Download figure Open in new tab Figure 4. Receiver Operating Characteristic (ROC) curves for (1) the best-performing EMG-based measure (i.e., X1) and cognitive measure (i.e., z-score on the Mini-Mental State Exam [MMSE]) for differentiating APOE-ε4 carriers from noncarriers, out of all 14 EMG-based measures and three cognitive measures, and (2) the combination of all 14 EMG-based measures (i.e., X1-X14), based on multivariate classification using penalized logistic regression. The classification model based on all EMG-based measures showed an AUC of 0.91, with 0.82 accuracy, 0.88 sensitivity, and 0.70 specificity, for differentiating APOE- ε4 carriers from noncarriers. The coefficients of the predictors, reflecting the relative importance of each EMG-based measure to the outcome, are shown in Table 4 . In addition to these EMG-based measures, age also comprised an important contributor to the classification (coefficient = -1.45), whereas the contribution of sex was more trivial (coefficient = 0.50). Taken together the ROC statistics for individual EMG measures and the relative importance of these measures in the multivariate classification model, X1, X2, and X11 appear to have overall the greatest discriminatory power for differentiating APOE- ε4 carriers from noncarriers (AUC ≥ 0.60; |coefficient| ≥ 0.65). These measures included two muscle-specific metrics (X1, reflecting the upper lip muscle characteristics; X2, reflecting the lower lip muscle characteristics) and one construct-specific metric (X11, reflecting the frequency of lower lip muscles). 3.4 Correlation of EMG-based measures with blood-based biomarkers For the EMG-based measures with the highest discriminatory power in classifying APOE-ε4 carriers versus noncarriers (i.e., X1, X2, X11), the corresponding biological correlates were identified as those biomarkers exhibiting an absolute correlation coefficient greater than 0.3, indicating a large effect size. 110 Accordingly, the biological correlates of X1 included the total and LDL cholesterol. An increase of X1 (reflecting increased amplitude, irregularity, and frequency of upper lip muscles, as observed in APOE- ε4 carriers from Figures 1 and 3 ) was correlated with decreases in total and LDL cholesterol. The biological correlates of X2 included glucose, hemoglobin, total and LDL cholesterol, as well as vitamin B12. A decrease of X2 (reflecting increased amplitude, irregularity, and frequency of upper lip muscles, as observed in APOE- ε4 carriers from Figures 1 and 3 ) was correlated with decreased glucose, hemoglobin, total and LDL cholesterol and increased vitamin B12. The biological correlates of X11 were triglycerides, vitamin B12, and serum folate. An increase of X11 (reflecting increased frequency of lower lip muscles, as observed in APOE- ε4 carriers from Figures 1 and 3 ) was correlated with decreased triglycerides and increased vitamin B12 and serum folate. 4. Discussion This study investigated the influence of a known genetic risk factor for AD – APOE -ε4 – on the neuromuscular function of speech-related muscles. Features extracted from craniofacial muscles outperformed cognitive measures in classifying APOE -ε4 carriers from noncarriers. We identified two potential neuromuscular risk factors for AD:increased motor unit recruitment and synchronization. These risk factors are likely associated with accelerated muscle fatigue, driven by APOE- ε4 -mediated changes in the central nervous system and neuromuscular function, potentially linked to mitochondrial dysfunction, oxidative stress, and impaired cellular metabolism. 4.1 Speech-based neuromuscular risk factors for AD Although APOE- ε4 is a known risk factor for cognitive decline and AD, its effects on motor function remain underexplored. This study reveals altered neuromuscular function in APOE- ε4 carriers during speech, marked by increased amplitude, frequency, complexity, and irregularity of myoelectric signals across facial and jaw muscles ( Figure 1 ). These patterns suggest a shift in motor unit recruitment strategies, characterized by increased activation of larger, fast-conducting motor units and greater synchronization of smaller motor units. Both are well-documented markers of neuromuscular fatigue, 91 , 111 potentially reflecting a compensatory response to early metabolic inefficiencies or motor unit remodeling associated with APOE- ε4 . This adaptation may help sustain force production but could also contribute to premature fatigue and altered motor control dynamics. These adaptations may also lead to higher frequency of muscle activity 112 and increased heterogeneity in the physiological properties of recruited motor unit pools, resulting in more complex and irregular firing patterns. 4.2 Biological correlates of neuromuscular changes The link between APOE- ε4 -associated neuromuscular changes and muscle fatigue is reinforced by biochemical correlates of EMG features. Key distinguishing factors (X1: global characteristics of upper lip muscles; X2: global characteristics of lower lip muscles; X11: frequency of lower lip muscles) correlate with total and LDL cholesterol, triglycerides, hemoglobin, glucose, vitamin B12, and serum folate. These biomarkers have critical implications for mitochondrial function and oxidative stress. Cholesterol and triglycerides shape mitochondrial membranes and influence energy production and oxidative stress 113 — processes often dysregulated in APOE- ε4 carriers due to altered lipid metabolism. 114 , 115 The accumulation of these metabolites can lead to increased group III and IV afferent feedback, necessitating compensatory increases in motor unit recruitment and discharge rates to maintain task demands. 116 – 118 Vitamin B12 and folate play essential roles in one-carbon metabolism and homocysteine regulation, affecting DNA synthesis, methylation reactions, and overall cellular energy balance. 119 – 121 Deficiencies in these vitamins can lead to elevated homocysteine levels, a risk factor for cognitive decline, and potentially contribute to the accelerated muscle fatigue observed in APOE- ε4 carriers. 113 Finally, hemoglobin is essential for oxygen transport and mitochondrial ATP production. 121 , 122 Impaired oxygen delivery can shift metabolism toward anaerobic glycolysis, leading to lactate buildup and muscle fatigue. 113 4.3 Cellular mechanisms underlying neuromuscular dysfunction in APOE-ε4 carriers At the molecular level, APOE- ε4 disrupts mitochondrial pathways involved in electron transport, ATP synthesis, and thermogenesis, 123 contributing to accelerated muscle fatigue and motor decline. Moreover, APOE- ε4 carriers exhibit heightened oxidative stress, particularly in synaptosomal membranes, leading to neuronal damage, motor neuron degeneration, and astrocyte dysfunction. 123 – 129 The interplay of oxidative stress, mitochondrial inefficiency, and disrupted synaptic signaling might have collectively influenced neuromuscular function in APOE- ε4 carriers in the current study, aligning with previous research. 29 , 130 , 131 Separately, the APOE- ε4 allele significantly impacts calcium ion dynamics and excitation-contraction coupling, potentially disrupting motor unit recruitment and synchronization. Studies have linked APOE- ε4 to mitochondrial calcium overload, demonstrated in Neuro-2A cells and rat hippocampal neurons. 2 , 132 This dysregulation compromises mitochondrial bioenergetics and ATP production, exacerbating oxidative stress and triggering pro-apoptotic pathways, contributing to neuronal and motor dysfunction. 2 Additionally, calcium homeostasis disruptions extend to the neuromuscular junction, where precise calcium signaling is essential for effective excitation-contraction coupling. APOE- ε4 -associated calcium dysregulation may impair the timing and strength of muscle contractions, further exacerbating motor unit recruitment and synchronization deficits. In our study, blood calcium levels were marginally lower in APOE- ε4 carriers compared to noncarriers (p = 0.05). While blood calcium levels provide limited insight into intracellular calcium ion dynamics, this finding suggests a potential systemic effect of APOE- ε4 on calcium homeostasis, warranting further investigation. 4.4 Neuronal hyperexcitability and neuromuscular compensation Given the role of APOE- ε4 in mitochondrial dysfunction, oxidative stress, and cellular metabolism, and their connection to muscle fatigue, the altered muscle activity observed in APOE- ε4 carriers may represent a compensatory response to sustain function despite energy deficits and neuromuscular inefficiencies. Supporting this, previous research indicates that iPSC-derived neurons from APOE- ε4 carriers exhibit higher excitatory current frequencies compared to isogenic APOE- ε3 control neurons. 133 , 134 This hyperexcitability may further disrupt motor function by altering the balance between excitatory and inhibitory signaling within motor circuits. Together, these metabolic and neuromuscular changes likely contribute to both increased muscle fatigue and adaptive shifts in motor unit behavior. 4.5 EMG-based neuromuscular markers: a potential risk indicator for AD APOE- ε4 -driven changes in EMG measures, including increased motor unit recruitment and synchronization, may serve as potential neuromuscular risk factors for AD. These measures outperformed standard cognitive tests in distinguishing APOE- ε4 carriers from noncarriers ( Table 4 and Figure 4 ). Furthermore, while MMSE showed the best cognitive test accuracy, it was more influenced by age, unlike EMG metrics—especially those from lip muscles—which were less age-dependent. These findings support EMG-based markers as sensitive, age-resilient indicators of subclinical motor decline, with potential to enhance early AD detection before cognitive symptoms emerge. 4.6 Sex-dependent impact of APOE-ε4 on neuromuscular function A sex-specific pattern emerged in craniofacial muscle connectivity ( Figure 1 ): female carriers showed a global decrease, while male carriers exhibited an increase, relative to noncarriers. This divergence may reflect an interaction between sex and APOE- ε4 genotype. In general, female APOE- ε4 carriers face a substantially higher risk of developing AD compared to male carriers. 135 Women with the APOE- ε4 allele experience more severe AD progression, with notable differences in neurological, biomarker, and pathological markers. 136 – 138 Compared to male carriers, female APOE- ε4 carriers demonstrate: 1) accelerated cognitive and functional decline, with a greater likelihood of MCI and AD progression 136 , 138 ; 2) a fourfold increased risk of AD between ages 65–75 compared to noncarriers 104 , 136 , 138 ; 3) higher cerebrospinal fluid total tau levels, indicative of greater neuronal degeneration 136 , 137 ; 4) more pronounced Aβ deposition, tau accumulation, and reduced glucose metabolism in key memory-related brain regions 137 ; 5) faster rates of brain structural atrophy compared to male carriers 139 ; and 6) greater disruptions in brain connectivity and metabolic function, contributing to their heightened vulnerability to AD pathology. 103 , 137 The interaction between sex and APOE- ε4 is likely driven by a complex interplay of genetic, hormonal, 140 vascular, 141 and metabolic, 104 factors. Notably, female APOE- ε4 carriers show greater mitochondrial dysfunction, 104 which may drive motor unit loss or remodeling, weakening muscle coupling and reducing functional connectivity. This impact of APOE- ε4 in males appears less pronounced. Their motor unit function is likely better preserved, providing a sufficient pool of intact motor units to maintain effective muscle coordination. Their increased functional connectivity may represent a compensatory strategy—enhancing synchronous muscle activation to boost system “stiffness” and offset fatigue-related declines in performance. Sex-specific differences in functional connectivity may limit its utility in distinguishing APOE- ε4 carriers from noncarriers. Opposing trends in males and females can cancel each other out when analyzed together, reducing the discriminatory power of this measure. This likely explains why functional connectivity (X4) has a near-zero coefficient in the penalized logistic regression model ( Table 4 ). These findings highlight the importance of stratifying analyses by sex to better capture genotype-specific neuromuscular risk factors. 4.7 Differential effects of APOE-ε4 on craniofacial musculature This study reveals that APOE- ε4 has a greater impact on facial rather than mandibular muscles. Facial muscles—especially those controlling lip movement—contain more slow-twitch fibers and exhibit greater degrees of freedom, while mandibular muscles are more robust, fast-twitch dominant. 142 , 143 Additionally, facial muscles primarily receive contralateral cortical input, in contrast to the bilateral innervation of mandibular muscles. 144 – 146 These anatomical and neurophysiological differences may make the facial musculature more vulnerable to APOE- ε4 -related neuromuscular disruption, explaining the more pronounced effects observed. It is also important to note that speech production requires moderate muscle activation, raising the question of whether the observed effects reflect APOE- ε4 pathology or task-specific demands. In this study, the speech stimuli heavily featured bilabial plosives, requiring strong and coordinated activation of lip muscles, but also involving jaw opening and closing movements. This may place a greater relative demand on lip muscles, potentially accelerating muscle fatigue and making them appear disproportionately affected by APOE- ε4 . Consequently, while the observed pattern may partly reflect a task-induced effect, it remains unclear whether the greater involvement of facial versus mandibular muscles directly results from APOE- ε4 pathology or is influenced by the motor demands of the task. Further studies utilizing varied speech and non-speech motor tasks are needed to disentangle APOE- ε4 -related changes from task-driven effects. 4.8 Limitations and future directions Future studies using a wider range of speech stimuli are needed to clarify the specific effects of APOE- ε4 on orofacial muscles. In addition, Figure 3 shows considerable variability in EMG measures within each genotype group. While sex and agewere accounted for, other confounding factors may also contribute. Identifying these sources of variability will be key in future research. Conflicts of Interest J.R.G has served as a paid consultant for several pharmacological and speech technology companies including Biogen, Google, and Modality.AI, Inc. D.H.S has held leadership or fiduciary role in Niji Corp, Smart Ion, and Salat Research Consulting. S.E.R consulted for Daewoong Pharmaceuticals, Allyx Therapeutics, BioVie, Bob’s Last marathon, Cortexyme, Merck, Jocasta, Sage Therapeutics, Vandria, Foster & Eldredge. The other authors report no conflicts of interest. Funding Sources This work was supported by NIH-NIDCD K23DC019179 (PI: Eshghi), the ASHFoundation Clinical Research Grant (PI: Eshghi), the A2Collective grant from Massachusetts AI and Technology Center for Connected Care in Aging and Alzheimer’s Disease (PI: Eshghi), ASHFoundation New Century Scholars Research Grant (PI: Rong), NIH-NIDCD K24DC016312 (PI: Green), and NIH-NIDCD R01DC021446 (PI: Connaghan, Green). Consent Statement Recruitment and consent procedures adhered to Mass General Brigham Healthcare (MGH) and HIPAA regulations. The study was approved by the MGB Institutional Review Board (IRB Protocol #2021P001460). Before participating in testing, administrators thoroughly explained the experimental tasks and procedures, addressing any questions participants had. Relevant consent forms were provided, and each form was reviewed point by point with potential participants by a research staff member prior to obtaining their signature. Participants were explicitly informed of their right to withdraw from the study at any time during the experiment without any consequences and all participants provided written informed consent prior to participation. Data Availability The raw speech recordings generated during the current study contain personally identifiable information and cannot be shared publicly for privacy and ethical reasons. A de-identified dataset comprising the extracted acoustic features that support the findings of this study is available from the corresponding author upon reasonable request. Author Contribution M.E. served as the Principal Investigator and played a leading role in study conceptualization and design, data collection, data analysis, interpretation of findings, and manuscript writing. P.R. contributed to data analysis and manuscript writing. M.D. reviewed the manuscript and provided feedback on data analysis and statistical methodology. H.S. assisted with the annotation of audio signals aligned with EMG recordings. B.D.R. supported data collection, contributed to IRB submission and approval, reviewed the manuscript, and provided feedback. N.V.B. assisted with data collection, reviewed the manuscript, and provided feedback. D.H.S. contributed to participant characterization, manuscript review, and feedback. S.E.A. was involved in participant characterization and also reviewed and provided feedback on the manuscript. J.R.G. contributed to study conceptualization and design, interpretation of findings, and also reviewed the manuscript, and provided feedback. 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