Data-driven sequence of cognitive decline in people with Parkinson's disease.

BACKGROUND: Understanding the sequential progression of cognitive impairments in Parkinson's disease (PD) is crucial for elucidating neuropathological underpinnings, refining the assessment of PD-related cognitive decline stages and enhancing early identification for targeted interventions. The first aim of this study was to use an innovative event-based modeling (EBM) analytic approach to estimate the sequence of cognitive declines in PD. The second aim was to validate the EBM by examining associations with EBM-derived individual-specific estimates of cognitive decline severity and performance on independent cognitive screening measures. METHODS: This cross-sectional observational study included 99 people with PD who completed a neuropsychological battery. Individuals were classified as meeting the criteria for mild cognitive impairment (PD-MCI) or subtle cognitive decline by consensus. An EBM was constructed to compare cognitively healthy individuals with those with PD-MCI or subtle cognitive disturbances. Multivariable linear regression estimated associations between the EBM-derived stage of cognitive decline and performance on two independent cognitive screening tests. RESULTS: The EBM estimated that tests assessing executive function and visuospatial ability become abnormal early in the sequence of PD-related cognitive decline. Each higher estimated stage of cognitive decline was associated with approximately 0.24 worse performance on the Dementia Rating Scale (p<0.001) and 0.26 worse performance on the Montreal Cognitive Assessment (p<0.001) adjusting for demographic and clinical variables. CONCLUSION: Findings from this study will have important clinical implications for practitioners, on specific cognitive tests to prioritise, when conducting neuropsychological evaluations with people with PD. Results also highlight the importance of frontal-subcortical system disruption impacting executive and visuospatial abilities.

Impact of autism genetic risk on brain connectivity: a mechanism for the female protective effect.

The biological mechanisms underlying the greater prevalence of autism spectrum disorder in males than females remain poorly understood. One hypothesis posits that this female protective effect arises from genetic load for autism spectrum disorder differentially impacting male and female brains. To test this hypothesis, we investigated the impact of cumulative genetic risk for autism spectrum disorder on functional brain connectivity in a balanced sample of boys and girls with autism spectrum disorder and typically developing boys and girls (127 youth, ages 8-17). Brain connectivity analyses focused on the salience network, a core intrinsic functional connectivity network which has previously been implicated in autism spectrum disorder. The effects of polygenic risk on salience network functional connectivity were significantly modulated by participant sex, with genetic load for autism spectrum disorder influencing functional connectivity in boys with and without autism spectrum disorder but not girls. These findings support the hypothesis that autism spectrum disorder risk genes interact with sex differential processes, thereby contributing to the male bias in autism prevalence and proposing an underlying neurobiological mechanism for the female protective effect.

A neurogenetic analysis of female autism.

Females versus males are less frequently diagnosed with autism spectrum disorder (ASD), and while understanding sex differences is critical to delineating the systems biology of the condition, female ASD is understudied. We integrated functional MRI and genetic data in a sex-balanced sample of ASD and typically developing youth (8-17 years old) to characterize female-specific pathways of ASD risk. Our primary objectives were to: (i) characterize female ASD (n = 45) brain response to human motion, relative to matched typically developing female youth (n = 45); and (ii) evaluate whether genetic data could provide further insight into the potential relevance of these brain functional differences. For our first objective we found that ASD females showed markedly reduced response versus typically developing females, particularly in sensorimotor, striatal, and frontal regions. This difference between ASD and typically developing females does not resemble differences between ASD (n = 47) and typically developing males (n = 47), even though neural response did not significantly differ between female and male ASD. For our second objective, we found that ASD females (n = 61), versus males (n = 66), showed larger median size of rare copy number variants containing gene(s) expressed in early life (10 postconceptual weeks to 2 years) in regions implicated by the typically developing female > female functional MRI contrast. Post hoc analyses suggested this difference was primarily driven by copy number variants containing gene(s) expressed in striatum. This striatal finding was reproducible among n = 2075 probands (291 female) from an independent cohort. Together, our findings suggest that striatal impacts may contribute to pathways of risk in female ASD and advocate caution in drawing conclusions regarding female ASD based on male-predominant cohorts.

Mapping the rest of the human connectome: Atlasing the spinal cord and peripheral nervous system.

The emergence of diffusion, structural, and functional neuroimaging methods has enabled major multi-site efforts to map the human connectome, which has heretofore been defined as containing all neural connections in the central nervous system (CNS). However, these efforts are not structured to examine the richness and complexity of the peripheral nervous system (PNS), which arguably forms the (neglected) rest of the connectome. Despite increasing interest in an atlas of the spinal cord (SC) and PNS which is simultaneously stereotactic, interactive, electronically dissectible, scalable, population-based and deformable, little attention has thus far been devoted to this task of critical importance. Nevertheless, the atlasing of these complete neural structures is essential for neurosurgical planning, neurological localization, and for mapping those components of the human connectome located outside of the CNS. Here we recommend a modification to the definition of the human connectome to include the SC and PNS, and argue for the creation of an inclusive atlas to complement current efforts to map the brain's human connectome, to enhance clinical education, and to assist progress in neuroscience research. In addition to providing a critical overview of existing neuroimaging techniques, image processing methodologies and algorithmic advances which can be combined for the creation of a full connectome atlas, we outline a blueprint for ultimately mapping the entire human nervous system and, thereby, for filling a critical gap in our scientific knowledge of neural connectivity.

Sex Differences in Functional Connectivity of the Salience, Default Mode, and Central Executive Networks in Youth with ASD.

Autism spectrum disorder (ASD) is associated with the altered functional connectivity of 3 neurocognitive networks that are hypothesized to be central to the symptomatology of ASD: the salience network (SN), default mode network (DMN), and central executive network (CEN). Due to the considerably higher prevalence of ASD in males, however, previous studies examining these networks in ASD have used primarily male samples. It is thus unknown how these networks may be differentially impacted among females with ASD compared to males with ASD, and how such differences may compare to those observed in neurotypical individuals. Here, we investigated the functional connectivity of the SN, DMN, and CEN in a large, well-matched sample of girls and boys with and without ASD (169 youth, ages 8-17). Girls with ASD displayed greater functional connectivity between the DMN and CEN than boys with ASD, whereas typically developing girls and boys differed in SN functional connectivity only. Together, these results demonstrate that youth with ASD exhibit altered sex differences in these networks relative to what is observed in typical development, and highlight the importance of considering sex-related biological factors and participant sex when characterizing the neural mechanisms underlying ASD.

Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach.

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction for a wide variety of images.

Mild cognitive impairment and structural brain abnormalities in a sexagenarian with a history of childhood traumatic brain injury.

In this report, we present a case study involving an older, female patient with a history of pediatric traumatic brain injury (TBI). Magnetic resonance imaging and diffusion tensor imaging volumes were acquired from the volunteer in question, her brain volumetrics and morphometrics were extracted, and these were then systematically compared against corresponding metrics obtained from a large sample of older healthy control (HC) subjects as well as from subjects in various stages of mild cognitive impairment (MCI) and Alzheimer disease (AD). Our analyses find the patient's brain morphometry and connectivity most similar to those of patients classified as having early-onset MCI, in contrast to HC, late MCI, and AD samples. Our examination will be of particular interest to those interested in assessing the clinical course in older patients having suffered TBI earlier in life, in contradistinction to those who experience incidents of head injury during aging.

Acute glucose and lactate metabolism are associated with cognitive recovery following traumatic brain injury.

Traumatic brain injury (TBI) is associated with acute cerebral metabolic crisis (ACMC). ACMC-related atrophy appears to be prominent in frontal and temporal lobes following moderate-to-severe TBI. This atrophy is correlated with poorer cognitive outcomes in TBI. The current study investigated ability of acute glucose and lactate metabolism to predict long-term recovery of frontal-temporal cognitive function in participants with moderate-to-severe TBI. Cerebral metabolic rate of glucose and lactate were measured by the Kety-Schmidt method on days 0-7 post-injury. Indices of frontal-temporal cognitive processing were calculated for six months post-injury; 12 months post-injury; and recovery (the difference between the six- and 12-month scores). Glucose and lactate metabolism were included in separate regression models, as they were highly intercorrelated. Also, glucose and lactate values were centered and averaged and included in a final regression model. Models for the prediction frontal-temporal cognition at six and 12 months post-injury were not significant. However, average glucose and lactate metabolism predicted recovery of frontal-temporal cognition, accounting for 23% and 22% of the variance, respectively. Also, maximum glucose metabolism, but not maximum lactate metabolism, was an inverse predictor in the recovery of frontal-temporal cognition, accounting for 23% of the variance. Finally, the average of glucose and lactate metabolism predicted frontal-temporal cognitive recovery, accounting for 22% of the variance. These data indicate that acute glucose and lactate metabolism both support cognitive recovery from TBI. Also, our data suggest that control of endogenous fuels and/or supplementation with exogenous fuels may have therapeutic potential for cognitive recovery from TBI.

Envisioning the future of 'big data' biomedicine.

Through the increasing availability of more efficient data collection procedures, biomedical scientists are now confronting ever larger sets of data, often finding themselves struggling to process and interpret what they have gathered. This, while still more data continues to accumulate. This torrent of biomedical information necessitates creative thinking about how the data are being generated, how they might be best managed, analyzed, and eventually how they can be transformed into further scientific understanding for improving patient care. Recognizing this as a major challenge, the National Institutes of Health (NIH) has spearheaded the "Big Data to Knowledge" (BD2K) program - the agency's most ambitious biomedical informatics effort ever undertaken to date. In this commentary, we describe how the NIH has taken on "big data" science head-on, how a consortium of leading research centers are developing the means for handling large-scale data, and how such activities are being marshalled for the training of a new generation of biomedical data scientists. All in all, the NIH BD2K program seeks to position data science at the heart of 21st Century biomedical research.

Connected brains and minds--The UMCD repository for brain connectivity matrices.

We describe the USC Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an interactive web-based platform for brain connectivity matrix sharing and analysis. The site enables users to download connectivity matrices shared by other users, upload matrices from their own published studies, or select a specific matrix and perform a real-time graph theory-based analysis and visualization of network properties. The data shared on the site span a broad spectrum of functional and structural brain connectivity information from humans across the entire age range (fetal to age 89), representing an array of different neuropsychiatric and neurodegenerative disease populations (autism spectrum disorder, ADHD, and APOE-4 carriers). An analysis combining 7 different datasets shared on the site illustrates the diversity of the data and the potential for yielding deeper insight by assessing new connectivity matrices with respect to population-wide network properties represented in the UMCD.

MGH-USC Human Connectome Project datasets with ultra-high b-value diffusion MRI.

The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.

The Tapley and Bryden test of performance differences between the hands: The original data, newer data, and the relation to pegboard and other tasks.

Tapley and Bryden (T&B)'s 1985 circle-marking task is a group-administered task assessing performance differences between the hands. The bimodal distribution clearly separates self-described right- and left-handers. Using Phil's original datafiles we analyse the test in more detail, providing raw scores for each hands which are useful forensically, and we provide reliability estimates. Van Horn's unpublished 1992 PhD thesis studied T&B tasks and Annett pegboards varying in difficulty. A striking finding, that Phil Bryden called "the Van Horn problem," was that hand differences (R - L) were unrelated to task difficulty. That result was the starting point for Pamela Bryden's 1998 thesis, firstly replicating Van Horn, but then showing that task difficulty did relate to hand differences for Grooved pegboards. Pamela Bryden's model for those effects is presented here. Comparing across tasks, the T&B and pegboard tasks showed almost complete consistency for direction of handedness. Likewise, within each task, degree of handedness intercorrelated strongly across variants. In strong contrast, degree of handedness for T&B tasks showed minimal correlation with degree of handedness for pegboards. At the highest level, therefore, direction of handedness is consistent within individuals (conventional right and left handedness), but there are separable processes determining dominant-non-dominant hands differences for each particular task.

The DTI connectivity of the human claustrum.

The origin, structure, and function of the claustrum, as well as its role in neural computation, have remained a mystery since its discovery in the 17th century. Assessing the in vivo connectivity of the claustrum may bring forth useful insights with relevance to model the overall functionality of the claustrum itself. Using structural and diffusion tensor neuroimaging in N = 100 healthy subjects, we found that the claustrum has the highest connectivity in the brain by regional volume. Network theoretical analyses revealed that (a) the claustrum is a primary contributor to global brain network architecture, and that (b) significant connectivity dependencies exist between the claustrum, frontal lobe, and cingulate regions. These results illustrate that the claustrum is ideally located within the human central nervous system (CNS) connectome to serve as the putative "gate keeper" of neural information for consciousness awareness. Our findings support and underscore prior theoretical contributions about the involvement of the claustrum in higher cognitive function and its relevance in devastating neurological disease.

Longitudinal quantification and visualization of intracerebral haemorrhage using multimodal magnetic resonance and diffusion tensor imaging.

OBJECTIVE: To demonstrate a set of approaches using diffusion tensor imaging (DTI) tractography whereby pathology-affected white matter (WM) fibres in patients with intracerebral haemorrhage (ICH) can be selectively visualized. METHODS: Using structural neuroimaging and DTI volumes acquired longitudinally from three representative patients with ICH, the spatial configuration of ICH-related trauma is delineated and the WM fibre bundles intersecting each ICH lesion are identified and visualized. Both the extent of ICH lesions as well as the proportion of WM fibres intersecting the ICH pathology are quantified and compared across subjects. RESULTS: This method successfully demonstrates longitudinal volumetric differences in ICH lesion load and differences across time in the percentage of fibres which intersect the primary injury. CONCLUSIONS: Because neurological conditions such as intracerebral haemorrhage (ICH) frequently exhibit pathology-related effects which lead to the exertion of mechanical pressure upon surrounding tissues and, thereby, to the deformation and/or displacement of WM fibres, DTI fibre tractography is highly suitable for assessing longitudinal changes in WM fibre integrity and mechanical displacement.

Biophysical modulation and robustness of itinerant complexity in neuronal networks.

Transient synchronization of bursting activity in neuronal networks, which occurs in patterns of metastable itinerant phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neuronal circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, can emerge in an inhibitory neuronal population when the neurons' intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neuronal population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of pyramidal population oscillations and the features of emergent metastability in the inhibitory population. In addition, we introduce a method to characterize collective transitions in metastable networks. Finally, we discuss potential applications of this study in mechanistically understanding cortical network dynamics.

Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?

Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions. Through better data integration, feature identification, knowledge representation, validation, etc., neuroinformaticists, are ideally suited to bring clarity, context, and explainabilty to the study of sports-related head injuries in males and in females, and helping to define recovery.

Widespread Associations between Behavioral Metrics and Brain Microstructure in ASD.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. A diagnosis of ASD is provided by a clinician following cognitive and behavioral evaluations, but there is currently no biomarker associating these metrics with neurological changes. Our lab has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased in ASD individuals. By associating these differences with performance on cognitive and behavioral tests, we can evaluate which tests most reveal changes in the brain. Analyzing 273 participants (148 with ASD) ages 8-to-17 (49% female) through an NIH-sponsored Autism Centers of Excellence (ACE) network (Grant#: MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics. Analyzing data from all participants, conduction velocity but not g-ratio was significantly associated with many behavioral metrics. However, this pattern was reversed when looking solely at ASD participants. This reversal may suggest that the mechanism underlying differences between autistic and non-autistic individuals may be distinct from the mechanism underlying ASD behavioral severity. Two additional machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. By associating neuroimaging metrics with ASD, it may be possible to measure and identify individuals at high risk of ASD before behavioral tests can detect them.

Conduction Velocity, G-ratio, and Extracellular Water as Microstructural Characteristics of Autism Spectrum Disorder.

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a novel metric termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel neuroimaging metric, aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.