Decoding the brain: From neural representations to mechanistic models.

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their function relies on distributed circuits to continuously encode relevant environmental and body-state features, enabling other areas to decode (interpret) these representations for computing meaningful decisions and executing precise movements. Thus, the distributed brain can be thought of as a series of computations that act to encode and decode information. In this perspective, we detail important concepts of neural encoding and decoding and highlight the mathematical tools used to measure them, including deep learning methods. We provide case studies where decoding concepts enable foundational and translational science in motor, visual, and language processing.

COVID-19 lockdown effects on adolescent brain structure suggest accelerated maturation that is more pronounced in females than in males.

Adolescence is a period of substantial social-emotional development, accompanied by dramatic changes to brain structure and function. Social isolation due to lockdowns that were imposed because of the COVID-19 pandemic had a detrimental impact on adolescent mental health, with the mental health of females more affected than males. We assessed the impact of the COVID-19 pandemic lockdowns on adolescent brain structure with a focus on sex differences. We collected MRI structural data longitudinally from adolescents prior to and after the pandemic lockdowns. The pre-COVID data were used to create a normative model of cortical thickness change with age during typical adolescent development. Cortical thickness values in the post-COVID data were compared to this normative model. The analysis revealed accelerated cortical thinning in the post-COVID brain, which was more widespread throughout the brain and greater in magnitude in females than in males. When measured in terms of equivalent years of development, the mean acceleration was found to be 4.2 y in females and 1.4 y in males. Accelerated brain maturation as a result of chronic stress or adversity during development has been well documented. These findings suggest that the lifestyle disruptions associated with the COVID-19 pandemic lockdowns caused changes in brain biology and had a more severe impact on the female than the male brain.

Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction.

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we formulate biologically plausible mechanistic models of the circuit. In particular, we consider a linear circuit model, for which we derive an exact theoretical solution, and a nonnegative circuit model, which we examine through simulations. The latter largely predicts the ORN [Formula: see text] LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN [Formula: see text] LN and LN-LN synaptic counts and the emergence of different LN types. Functionally, we propose that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. We thus uncover a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, our study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations.

Mapping human brain charts cross-sectionally and longitudinally.

Brain scans acquired across large, age-diverse cohorts have facilitated recent progress in establishing normative brain aging charts. Here, we ask the critical question of whether cross-sectional estimates of age-related brain trajectories resemble those directly measured from longitudinal data. We show that age-related brain changes inferred from cross-sectionally mapped brain charts can substantially underestimate actual changes measured longitudinally. We further find that brain aging trajectories vary markedly between individuals and are difficult to predict with population-level age trends estimated cross-sectionally. Prediction errors relate modestly to neuroimaging confounds and lifestyle factors. Our findings provide explicit evidence for the importance of longitudinal measurements in ascertaining brain development and aging trajectories.

Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia.

Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.

From Fetus to Eight: the CHILD Cohort Study.

The CHILD Cohort Study is an active multi-center longitudinal, prospective, population pregnancy cohort study following Canadian infants from fetal life until adulthood. We hypothesized that early life physical and psychosocial environments interact with biological factors (e.g. immunologic, genetic, physiologic, and metabolic) influencing burdensome non-communicable disease outcomes, including asthma and allergic disorders, growth and development, cardio-metabolic health, and neurodevelopmental outcomes that manifest during the life-course. Detailed clinical and physiologic phenotyping at strategic intervals was complemented by environmental sampling, actigraphy and global positioning system measures, biological sampling including gut, breastmilk and nasal microbiome, nutritional studies, genetics, and epigenetic profiling. Of 3,454 families recruited from 2008 to 2012, study retention was 96.0% at 1-year, 93.2% at 5-years and 90.7% at 8-years. Data collection during the SARS-2 COVID-19 pandemic was partially completed via virtual visits. A sub-cohort was implemented, capturing detailed information on the prevalence and predictors of SARS-CoV-2 infection and the health and psychosocial impact of the pandemic on Canadian families. The 13-year clinical assessment launched in 2022 will be completed in 2025. Ultimately, the CHILD Cohort Study provides a data science platform designed to enable a deep understanding of early life factors associated with the development of chronic non-communicable diseases and multimorbidity.

Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models.

This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6-17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born preterm.

Pitfalls in the lab assessment of hypopituitarism.

The diagnostic approach to hypopituitarism involves many disciplines. Clinical symptoms rarely are specific. Imaging techniques are helpful but cannot prove the specific functional defects. Therefore, the definitive diagnosis of pituitary insufficiency is largely based on laboratory tests. However, also laboratory methods come with inherent limitations, and it is essential for the clinician to know and recognize typical pitfalls. Most factors potentially impairing the quality of hormone measurements are introduced in the preanalytical phase, i.e. before the hormones are measured by the laboratory. For example, the timing of blood drawing with respect to circadian rhythm, stress, and medication can have an influence on hormone concentrations. During the actual analysis of the hormones, cross-reactions with molecules present in the sample presenting the same or similar epitopes than the intended analyte may affect immunoassays. Interference can also come from heterophilic or human anti-animal antibodies. Unexpected problems can also be due to popular nutritional supplements which interfere with the measurement procedures. An important example in this respect is the interference from biotin. It became only clinically visible when the use of this vitamin became popular among patients. The extreme serum concentrations reached when patients take it as a supplement can lead to incorrect measurements in immunoassays employing the biotin-streptavidin system. To some extent, hormone analyses using liquid chromatography mass spectrometry (LCMS) can overcome problems, although availability and cost-effectiveness of this method still imposes restrictions. In the post-analytical phase, appropriateness of reference intervals and cut-offs with respect to the specific analytical method used is of outmost importance. Furthermore, for interpretation, additional biological and pharmacological factors like BMI, age and concomitant diseases must be considered to avoid misinterpretation of the measured concentrations. It is important for the clinician and the laboratory to recognize when one or more laboratory values do not match the clinical picture. In an interdisciplinary approach, the search for the underlying cause should be initiated.

Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach.

BACKGROUND: Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level. METHODS: Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks. RESULTS: We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms. CONCLUSIONS: Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.

Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders.

BACKGROUND: Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine. METHODS: We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort. RESULTS: Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable. CONCLUSIONS: Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

Gray matter atrophy is constrained by normal structural brain network architecture in depression.

BACKGROUND: There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression. METHODS: In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis. RESULTS: In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset. CONCLUSIONS: These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

"Pscore": A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics.

BACKGROUND: Quantitative magnetic resonance imaging (MRI) metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. PURPOSE: To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, "Pscore" to address this issue. STUDY TYPE: Retrospective cohort. POPULATION: Nine hundred and sixty-one healthy young adults (age: 22-35 years, females: 53%) from the Human Connectome Project. FIELD STRENGTH/SEQUENCE: 3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE. ASSESSMENT: The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate "Pscores"-which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values. STATISTICAL TESTS: ROI-wise distributions were assessed using log transformations, Zscore, and the "Pscore" methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95(%), PEV<5(%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (N = 100) using 100 iterations. RESULTS: The dMRI metric distributions were systematically non-Gaussian, including positively skewed (eg, mean and radial diffusivity) and negatively skewed (eg, fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95 ≠ 5%, PEV<5 ≠ 5%) whereas "Pscore" distributions were symmetric and balanced (PEV>95 = PEV<5 = 5%); even for small bootstrapped samples (average PEV > 95 ¯ = PEV < 5 ¯ = 5 ± 0 % [SD]). DATA CONCLUSION: The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed "Pscore" method may help estimating individual deviations more accurately in skewed normative data, even from small datasets. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1.

Meaningful cognitive change for the Minimal Assessment of Cognitive Function in Multiple Sclerosis.

BACKGROUND: There is limited information on interpretation of cognitive changes over time in multiple sclerosis (MS). OBJECTIVE: This study aimed to provide normative data for the assessment of statistically meaningful change in all tests of the Minimal Assessment of Cognitive Function in MS (MACFIMS). METHODS: We applied the reliable change methodology to a healthy Italian cohort, assessed with two alternate versions of the MACFIMS 1 year apart. We calculated confidence intervals of retest score variance using the reliable change index (RCI). Moreover, multivariable linear regression models adjusted for age, sex, education, and baseline score were built to calculate the regression-based change index (RB-CI). RESULTS: Overall, 200 healthy individuals were enrolled. Thresholds for interpreting change in each test were calculated. In the multivariable models, baseline score was associated with retest score in all tests (B from 0.439 to 0.760; p < 0.001). RB-CI can be calculated with data of the multivariable models. CONCLUSION: We provide normative data for reliable cognitive change evaluation for all the tests of the MACFIMS, which includes the Symbol Digit Modalities Test and Brief International Cognitive Assessment in MS, two widely used tools for screening and monitoring cognition in MS. Our findings can significantly improve the interpretation of cognitive changes in MS.

A Bayesian nonparametric meta-analysis model for estimating the reference interval.

A reference interval represents the normative range for measurements from a healthy population. It plays an important role in laboratory testing, as well as in differentiating healthy from diseased patients. The reference interval based on a single study might not be applicable to a broader population. Meta-analysis can provide a more generalizable reference interval based on the combined population by synthesizing results from multiple studies. However, the assumptions of normally distributed underlying study-specific means and equal within-study variances, which are commonly used in existing methods, are strong and may not hold in practice. We propose a Bayesian nonparametric model with more flexible assumptions to extend random effects meta-analysis for estimating reference intervals. We illustrate through simulation studies and two real data examples the performance of our proposed approach when the assumptions of normally distributed study means and equal within-study variances do not hold.

Development of robust normative data for the neuropsychological assessment of Greek older adults.

OBJECTIVE: Normative data for older adults may be tainted by inadvertent inclusion of undiagnosed individuals at the very early stage of a neurodegenerative process. To avoid this pitfall, we developed norms for a cohort of older adults without MCI/dementia at 3-year follow-up. METHODS: A randomly selected sample of 1041 community-dwelling individuals (age ≥ 65) received a full neurological and neuropsychological examination on two occasions [mean interval = 3.1 (SD = 0.9) years]. RESULTS: Of these, 492 participants (Group 1; 65-87 years old) were without dementia on both evaluations (CDR=0 and MMSE ≥ 26); their baseline data were used for norms development. Group 2 (n = 202) met the aforementioned criteria only at baseline, but not at follow-up. Multiple linear regressions included demographic predictors for regression-based normative formulae and raw test scores as dependent variables for each test variable separately. Standardized scaled scores and stratified discrete norms were also calculated. Group 2 performed worse than Group 1 on most tests (p-values < .001-.021). Education was associated with all test scores, age with most, and sex effects were consistent with the literature. CONCLUSIONS: We provide a model for developing sound normative data for widely used neuropsychological tests among older adults, untainted by potential early, undiagnosed cognitive impairment, reporting regression-based, scaled, and discrete norms for use in clinical settings to identify cognitive decline in older adults. Additionally, our co-norming of a variety of tests may enable intra-individual comparisons for diagnostic purposes. The present work addresses the challenge of developing robust normative data for neuropsychological tests in older adults.

Phonological fluency norms for Spanish middle-aged and older adults provided by the SCAND initiative (P, M, & R).

OBJECTIVE: Verbal fluency tests are quick and easy to administer neuropsychological measures and are regularly used in neuropsychological assessment. Additionally, phonological fluency is a widely used paradigm that is sensitive to cognitive impairment. This paper offers normative data of phonological verbal fluency (letters P, M, R) for Spanish middle- and older-aged adults, considering sociodemographic factors, and different measures such as the total number of words, errors (perseveration and intrusions), and 15 sec-segmented scores. METHOD: A total of 1165 cognitively unimpaired participants aged between 50 and 89 years old, participated in the study. Data for P were obtained for all participants. Letters M and R were also administered to a subsample of participants (852) aged 60 to 89 years. In addition, errors and words produced every 15 seconds were collected in the subsample. To verify the effect of sociodemographic variables, linear regression was used. Adjustments were calculated for variables that explained at least 5% of the variance (R2 ≥ .05). RESULTS: Means and standard deviations by age, scaled scores, and percentiles for all tests across different measures are shown. No determination coefficients equal to or greater than .05 were found for sex or age. The need to establish adjustments for the educational level was only found in some of the measures. CONCLUSIONS: The current norms provide clinically useful data to evaluate Spanish-speaking natives from Spain aged from 50 to 89 years. Specific patterns of cognitive impairment can be analyzed using these normative data and may be important in neuropsychological assessment.

Norms for neuropsychological tests in cognitively healthy French oldest old adults.

OBJECTIVE: Normal aging often leads to cognitive decline, and oldest old people, over 80 years old, have a 15% risk of developing neurodegenerative diseases. Therefore, it is important to have appropriate tools to assess cognitive function in old age. The study aimed to provide new norms for neuropsychological tests used to evaluate the cognitive abilities in people aged 80 years and older in France, focusing on the impact of education and gender differences. METHOD: 107 healthy participants with an average age of 85.2 years, with no neurological history or major cognitive deficits were included. A comprehensive neuropsychological assessment was performed, covering several cognitive functions such as memory, visuospatial abilities, executive functions, attention, processing speed, and praxis. RESULTS: Individuals with lower levels of education performed poorly on some tests and took longer to complete. Gender differences were observed, with women outperforming men in verbal episodic memory, while men showed better performance in visuoconstructive tasks. The participants showed lower performance in verbal episodic memory compared to norms established in previous French studies. In relation to executive functions, participants were slower to perform complex tasks than participants in previous studies. CONCLUSION: This study provides cognitive norms specifically adapted to the oldest old population, which differ from established norms for younger aging adults. It highlights the importance of including these norms in future clinical and scientific investigations. The findings underscore the importance of education on cognitive abilities and emphasize the need to consider gender differences when assessing cognitive functions in aging populations.

HIV and vicarious stigma in a cohort of people living with HIV in Italy: What happens when the stigma is fueled by healthcare providers?

Vicarious stigma shows how indirect stigmatizing experiences can lead people living with HIV (PLWH) to feel discriminated against. We enrolled 350 PLWH, who were administered a 17-item questionnaire to investigate a subjective experience of stigma experienced in the hospital care setting. We found that at least once 215 PLWH (61.4%) did not want the HIV exemption indicated on the prescription for a specialist medical visit, 232 PLWH (66.3%) never used their HIV-related exemption to make a specialist medical visit, 230 PLWH (65.7%) avoided undergoing a medical assessment outside the infectious disease clinics and 241 patients (68.9%) felt unwelcome during a specialist medical visit. Moreover, 241 patients (61.1%) had heard at least once stories of health workers who did not want to touch PLWH, 213 patients (60.9%) had heard stories at least once of PLWH who had been mistreated by hospital staff, 180 patients (51.4%) had at least once heard stories about PLWH being refused treatment and services and 257 patients (73.4%) had at least once heard stories about health workers talking publicly about PLWH. This is a little explored area, especially regarding the vicarious stigma faced by PLWH. Our findings indicate the importance of combating HIV-related stigma for the wellbeing of PLWH.

Normative values and factors affecting saphenous nerve responses in a south Asian population: a cross-sectional neurophysiological study at a tertiary care hospital in Pakistan.

BACKGROUND: The saphenous nerve, a sensory branch of the femoral nerve, is not commonly included in routine lower extremity nerve conduction studies due to a high frequency of non-recordable responses in healthy subjects. However, saphenous nerve conduction studies are sometimes utilized for the diagnostic assessment of isolated lumbosacral plexus, femoral, or saphenous mononeuropathies. Our study aims to determine normative saphenous nerve response values in a healthy Pakistani population and to investigate their associations with patient body mass index, age, and gender. METHODS: This cross-sectional descriptive study was undertaken over a 3­month period (May to July 2021) at a neurophysiology department of a tertiary care center in Pakistan. Healthy subjects underwent neurological examination, anthropometric measurements, and bilateral SN nerve conduction studies, with recording of peak-latency, peak-to-peak amplitude and conduction velocity. Statistical analyses and linear regression were conducted to evaluate associations between nerve conduction study variables and patient characteristics. Statistical analyses were also run to assess patient characteristics affecting recordability of saphenous nerve responses. A p-value < 0.05 was considered statistically significant. RESULTS: Among 117 subjects, 79.5% (n = 93) had recordable saphenous nerve responses. Median peak-latency, amplitude, and conduction velocity were 3.2 (3.0-3.3) m/s, 7.7 (5.8-9.9) uV, and 44.0 (42.0-47.0) m/s, respectively. Bilaterally absent responses were observed in 20.5% (n = 24) of subjects. Obese participants had a significantly higher number of absent saphenous responses (p = 0.033). Females had shorter peak-latency (p = 0.006) and higher conduction velocity (p = 0.012). CONCLUSIONS: Saphenous nerve responses can be used to assess unilateral femoral and saphenous nerve pathologies, provided they are recordable on the asymptomatic side for comparison. Absent bilateral saphenous nerve responses should be interpreted with caution given their prevalence in healthy individuals. Patient characteristics should be taken into consideration when interpreting recordable and nonrecordable saphenous nerve responses.

Frontal cortex neurometabolites and mobility in older adults: a preliminary study.

BACKGROUND: The frontal cortex, relevant to global cognition and motor function, is recruited to compensate for mobility dysfunction in older adults. However, the in vivo neurophysiological (e.g., neurometabolites) underpinnings of the frontal cortex compensation for mobility dysfunction remain poorly understood. The purpose of this study was to investigate the relationships among frontal cortex neurophysiology, mobility, and cognition in healthy older adults. METHODS: Magnetic Resonance Spectroscopy (MRS) quantified N-acetylasparate (tNAA) and total choline (tCho) concentrations and ratios in the frontal cortex in 21 older adults. Four inertial sensors recorded the Timed Up & Go (TUG) test. Cognition was assessed using the Flanker Inhibitory Control and Attention Test which requires conflict resolution because of response interference from flanking distractors during incongruent trials. Congruent trials require no conflict resolution. RESULTS: tNAA concentration significantly related to the standing (p = 0.04) and sitting (p = 0.03) lean angles. tCho concentration (p = 0.04) and tCho ratio (p = 0.02) significantly related to TUG duration. tCho concentration significantly related to incongruent response time (p = 0.01). tCho ratio significantly related to both congruent (p = 0.009) and incongruent (p < 0.001) response times. Congruent (p = 0.02) and incongruent (p = 0.02) Flanker response times significantly related to TUG duration. CONCLUSIONS: Altered levels of frontal cortex neurometabolites are associated with both mobility and cognitive abilities in healthy older adults. Identifying neurometabolites associated with frontal cortex compensation of mobility dysfunction could improve targeted therapies aimed at improving mobility in older adults.