Biology subjects

Turner, J. A.

Publications and source records attributed to Turner, J. A..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Towards Multimodal Longitudinal Analysis for Predicting Cognitive Decline Using Neuroimaging Biomarkers, Cognitive Assessments, and Demographic Data

Understanding and predicting cognitive decline in Alzheimers disease (AD) is crucial for timely intervention and management. While neuroimaging biomarkers and clinical assessments are valuable individually, their combined predictive power and interaction with demographic and cognitive variables remain underexplored. This study lays the groundwork for comprehensive longitudinal analyses by integrating neuroimaging markers and clinical data to predict cognitive changes over time. Using data from the Alzheimers Disease Neuroimaging Initiative (ADNI), we applied feature-driven supervised machine learning techniques for assessing cognitive decline predictability. We hypothesize that combining neuroimaging biomarkers with demographic and clinical assessment variables significantly improves the prediction of cognitive decline in Alzheimers disease. Our results show that while imaging biomarkers alone offer moderate predictive capabilities, including key clinical assessment and demographic variables in conjunction with imaging biomarkers significantly improves the model performance. Furthermore, our results indicate that non-imaging variables alone can serve as effective and cost-efficient predictors of cognitive decline. This study underscores the need for integrating multi-dimensional data in future longitudinal research to capture time-dependent patterns in cognitive decline and guide the development of targeted intervention strategies. We also introduce NeuroLAMA - an open and extensible data engineering and machine-learning system, to support the continued investigation by the community

neuroscience↗

bioRxiv2024

Template-driven dynamic functional network connectivity predicts medication response for major depression and bipolar disorders

The process of finding reliable treatment for major depression and bipolar disorder can be arduous. The myriad behavioral symptoms presented by patients and resistance to treatment from particular medication classes complicate standard diagnostic and prescription methodologies, often requiring multiple attempted treatments during which symptoms may still be present. Physiological information such as neuroimaging scans may help to alleviate some of the uncertainty surrounding diagnosis and treatment when incorporated into a clinical setting. Changes in functional magnetic resonance imaging show particular promise, as the incorporation of dynamical information may provide insights into physiological changes prior to static, structural changes. In this work, we present a novel method for generating robust and replicable dynamic functional network connectivity (dFNC) features from neuroimaging data using a template of dynamic states derived from a large, non-affected data set. We demonstrate that this template-driven dFNC approach expands on standard dFNC approaches by allowing for the derivation of a continuous state-contribution time series. We demonstrate that the derived biomarkers can support high predictive performance for the identification of medication class and non-responders while also expanding the set of biomarkers available for studying differences in mood disorder medication response.

neuroscience↗

bioRxiv2024

Addressing inconsistency in functional neuroimaging: A replicable data-driven multi-scale functional atlas for canonical brain networks

The advent of multiple neuroimaging methodologies has greatly aided in the conceptualization of large-scale functional brain networks in the field of cognitive neuroscience. However, there is inconsistency across studies in both nomenclature and the functional entities being described. There is a need for a unifying framework that standardizes terminology across studies while also bringing analyses and results into the same reference space. Here we present a whole-brain atlas of canonical functional brain networks derived from more than 100,000 resting-state fMRI datasets. These data-driven functional networks are highly replicable across datasets and capture information from multiple spatial scales. We have organized, labeled, and described the networks with terms familiar to the fields of cognitive and affective neuroscience in order to optimize their utility in future neuroimaging analyses and enhance the accessibility of new findings. The benefits of this atlas are not limited to future template-based or reference-guided analyses, but also extend to other data-driven neuroimaging approaches across modalities, such as those using blind independent component analysis (ICA). Future studies utilizing this atlas will contribute to greater harmonization and standardization in functional neuroimaging research.

neuroscience↗

bioRxiv2024

Functionally Adaptive Structural Basis Sets of the Brain: A Dynamic Fusion Approach

The precise relationship between brain structure and function has been investigated through a multitude of lenses, but one detail that is held constant across most neuroimaging studies in this space is the identification of a singular structural basis set of the brain, upon which functional activation signals can be reconstructed to examine the linkage between structure and function. Such basis sets can be considered "functionally independent", as they are derived through structural data alone and have no explicit association to functional data. Recent work in multimodal fusion has facilitated a more integrated view of structure-function linkages by enabling the equal contribution of both modalities to the joint decomposition, resulting in components that are independent within modality but co-vary closely across modalities. These existing symmetric fusion approaches thus identify structural bases given an associated functional context. In this work we consider an additional layer of precision to the investigation of structure-function coupling by studying these context-dependent linkages in a time-resolved manner. In other words, we ask which features of brain structure become (or remain) salient given the dynamically changing functional contexts (i.e., dynamic functional connectivity states, task structure, etc.) the brain may pass through during a given fMRI scan. We introduce "dynamic fusion", an ICA-based symmetric fusion approach that enables flexible, time-resolved linkages between brain structure and dynamic brain function. We show evidence that temporally resolved, and functionally contextualized, structural basis sets can accurately reflect dynamic functional processes and capture diagnostically relevant structure-functional coupling while detecting nuanced functionally-driven structural components that cannot be captured with traditionally computed structural bases. Lastly, differential analysis of component stability across repeated scans from a control cohort reveals organization of static and dynamic structure/function coupling falls along unimodal/transmodal hierarchical lines.

neuroscience↗

bioRxiv2024

Young guard cells function dynamically despite low mechanical anisotropy but gain efficiency during stomatal maturation in Arabidopsis thaliana

Stomata are pores at the leaf surface that enable gas exchange and transpiration. The signaling pathways that regulate the differentiation of stomatal guard cells and the mechanisms of stomatal pore formation have been characterized in Arabidopsis thaliana. However, the process by which stomatal complexes develop after pore formation into fully mature complexes is poorly understood. We tracked the morphogenesis of young stomatal complexes over time to establish characteristic geometric milestones along the path of stomatal maturation. Using 3D-nanoindentation coupled with finite element modeling of young and mature stomata, we found that despite having thicker cell walls than young guard cells, mature guard cells are more energy efficient with respect to stomatal opening, potentially attributable to the increased mechanical anisotropy of their cell walls and smaller changes in turgor pressure between the closed and open states. Comparing geometric changes in young and mature guard cells of wild-type and cellulose-deficient plants revealed that although cellulose is required for normal stomatal maturation, mechanical anisotropy appears to be achieved by the collective influence of cellulose and additional wall components. Together, these data elucidate the dynamic geometric and biomechanical mechanisms underlying the development process of stomatal maturation. SignificanceWe examined how young stomata develop into mature complexes and the functional consequences of stomatal maturation, establishing geometric milestones and detecting mechanical changes in wall anisotropy and turgor pressure during stomatal maturation. We show that mature guard cells exert less energy and require a lower increase in turgor pressure to open the pore, likely due to higher wall anisotropy that results from the patterned deposition and/or remodeling of cellulose and other wall components.

plant biology↗

bioRxiv2024

Time-varying Spatial Propagation of Brain Networks in fMRI data

Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel-wise network-specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated to blood oxygenation level-dependent (BOLD) signal for 6-8 seconds, whereas the visual network propagates much quicker. We also show that summaries of network specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between schizophrenia patients and controls within four large-scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia.

neuroscience↗

bioRxiv2023

A Confounder Controlled Machine Learning Approach: Group Analysis and Classification of Schizophrenia and Alzheimer's Disease using Resting-State Functional Network Connectivity

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimers disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patients age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females.

neuroscience↗

bioRxiv2022

Canonical and Replicable Multi-Scale Intrinsic Connectivity Networks in 100k+ Resting-State fMRI Datasets

Resting-state functional magnetic resonance imaging (rsfMRI) has shown considerable promise for improving our understanding of brain function and characterizing various mental and cognitive states in the healthy and disordered brain. However, the lack of accurate and precise estimations of comparable functional patterns across datasets, individuals, and ever-changing brain states in a way that captures both individual variation and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. We posit that using reliable network templates and advanced group-informed network estimation approaches to accurately and precisely obtain individualized (dynamic) networks that retain cross-subject correspondence while maintaining subject-specific information is one potential solution to overcome the aforementioned barrier when considering cross-study comparability, independence of subject-level estimates, the limited data available in single studies, and the low signal-to-noise ratio (SNR) of rsfMRI. Toward this goal, we first obtained a reliable and replicable network template. We combined rsfMRI data of over 100k individuals across private and public datasets and selected around 58k that meet quality control (QC) criteria. We then applied multi-model-order independent component analysis (ICA) and subsampling to obtain reliable canonical intrinsic connectivity networks (ICNs) across multiple spatial scales. The selected ICNs (i.e., network templates) were also successfully replicated by independently analyzing the data that did not pass the QC criteria, highlighting the robustness of our adaptive template to data quality. We next studied the feasibility of estimating the corresponding subject-specific ICNs using a multivariate-spatially constrained ICA as an example of group-informed network estimation approaches. The results highlight that several factors, including ICNs themselves, data length, and spatial resolution, play key roles in successfully estimating the ICNs at the subject level. Large-scale ICNs, in general, require less data to achieve a specific level of spatial similarity with their templates (as well as within- and between-subject spatial similarity). Moreover, increasing data length can reduce an ICNs subject-level specificity, suggesting longer scans might not always be desirable. We also show spatial smoothing can alter results, and the positive linear relationship we observed between data length and spatial smoothness (we posit that it is at least partially due to averaging over intrinsic dynamics or individual variation) indicates the importance of considering this factor in studies such as those focused on optimizing data length. Finally, the consistency in the spatial similarity between ICNs estimated using the full-length of data and subset of it across different data lengths may suggest that the lower within-subject spatial similarity in shorter data lengths is not necessarily only defined by lower reliability in ICN estimates; rather, it can also be an indication of brain dynamics (i.e., different subsets of data may reflect different ICN dynamics), and as we increase the data length, the result approaches the average (also known as static) ICN pattern, and therefore loses its distinctiveness.

neuroscience↗

bioRxiv2021

Multimodal IVA fusion for detection of linked neuroimaging biomarkers

With the increasing availability of large-scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross-modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross-modal sources in multiple datasets. In this work we utilized the multimodal independent vector analysis model in MISA to directly identify meaningful linked features across three neuroimaging modalities -- structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI -- in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (the sources/components) that capture age-associated decline, schizophrenia-related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality-specific brain-age deltas were evaluated for association with non-imaging variables. In addition, each set of linked sources reveals a corresponding set of multi-tissue spatial patterns that can be studied jointly.

neuroscience↗

bioRxiv2021

Cell twisting during desiccation reveals axial asymmetry in wall organization

Plant cell size and shape are tuned to their function and specified primarily by cellulose microfibril (CMF) patterning of the cell wall. Arabidopsis thaliana leaf trichomes are responsible for protecting plants against environmental elements and are unicellular structures that employ a highly anisotropic cellulose wall to extend and taper, generating pointed branches. During elongation, the mechanisms by which shifts in fiber orientation generate cells with predictable sizes and shapes are unknown. Specifically, the axisymmetric growth of trichome branches is often thought result from axisymmetric CMF patterning. Here, we analyzed the direction and degree of twist of branches after desiccation to reveal the presence of an asymmetric cell wall organization with a left-hand bias. CMF organization, quantified using computational modeling, suggests a limited reorientation of microfibrils during growth and maximum branch length limited by the wall axial stiffness. The model provides a mechanism for CMF asymmetry, which occurs after the branch bending stiffness becomes low enough that ambient bending affects the principal stresses. After this stage, the CMF synthesis results in a constant bending stiffness for longer branches. The resulting natural frequency of branches after a length of 200 m falls within the range of the sounds associated with many insects. Statement of significanceThe growth of plant cell walls is governed by the direction of cellulose synthesis but the factors that influence the overall wall anisotropy are only partially understood. The twist of leaf trichome branches after desiccation reveals a left-handed asymmetry in cell wall organization even though the geometry is axisymmetric. This surprising behavior provides information about the directionality of cellulose synthesis control in plant cells.

plant biology↗

bioRxiv2021

Moving Beyond the 'CAP' of the Iceberg: Intrinsic Connectivity Networks in fMRI are Continuously Engaging and Overlapping

Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.

neuroscience↗

bioRxiv2021

Protocol for mapping the spatial variability in cell wall mechanical bending behavior in living leaf pavement cells

An integrated, experimental-computational approach is presented to analyze the variation of elastic bending behavior in the primary cell wall of living Arabidopsis thaliana pavement cells and to measure turgor pressure in the cells quantitatively under different osmotic conditions. Mechanical properties, size and geometry of cells and internal turgor pressure greatly influence their morphogenesis. Computational models of plant morphogenesis require values for wall elastic modulus and turgor pressure but very few experiments were designed to validate the results using measurements that deform the entire thickness of the cell wall. Because new wall material is deposited from inside the cell, full-thickness deformations are needed to quantify relevant changes associated with cell development. The approach here uses laser scanning confocal microscopy to measure the three-dimensional geometry of a single pavement cell, and indentation experiments equipped with high magnification objective lens to probe the local mechanical responses across the same cell wall. These experimental results are matched iteratively using a finite element model of the experiment to determine the local mechanical properties, turgor pressure, and cell height. The resulting modulus distribution along the periclinal wall is shown to be nonuniform. These results are consistent with the characteristics of plant cell walls which have a heterogeneous organization. This research and the resulting model will provide a reference for future work associated with the heterogeneity and anisotropy of mechanical properties of plant cell walls in order to understand morphogenesis of the primary cell walls during growth and to predict quantitatively the magnitudes/directions of cell wall forces. One sentence summaryThe distribution of elastic modulus of the periclinal cell walls of living Arabidopsis epidermis is nonuniform as measured by bending the entire thickness of the wall. HighlightsO_LIExperimental characterization of the spatial distribution of elastic bending behavior across the periclinal wall C_LIO_LIQuantification of the turgor pressure of the living plant epidermal cells validated with osmotic treatments C_LIO_LIQuantification of the effect of cell geometry on the measured mechanical response C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=176 SRC="FIGDIR/small/432478v1_ufig1.gif" ALT="Figure 1"> View larger version (117K): org.highwire.dtl.DTLVardef@46fcfcorg.highwire.dtl.DTLVardef@8b0ab0org.highwire.dtl.DTLVardef@6aad31org.highwire.dtl.DTLVardef@1909d6a_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Real-time conversion of tissue-scale mechanical forces into an interdigitated growth pattern

The leaf epidermis is a dynamic biomechanical shell that integrates growth across spatial scales to influence organ morphology. Pavement cells, the fundamental unit of this tissue, morph irreversibly into highly lobed cells that drive planar leaf expansion. Here we define how tissue-scale cell wall tensile forces and the microtubule-cellulose synthase systems pattern interdigitated growth in real-time. A morphologically potent subset of cortical microtubules span the periclinal and anticlinal cell faces to pattern cellulose fibers that generate a patch of anisotropic wall. The result is local polarized growth that is mechanically coupled to the adjacent cell via a pectin-rich middle lamella, and this drives lobe formation. Finite element pavement cell models revealed cell wall tensile stress as an upstream patterning element that links cell- and tissue-scale biomechanical parameters to interdigitated growth. Cell lobing in leaves is evolutionarily conserved, occurs in multiple cell types, and is associated with important agronomic traits. Our general mechanistic models of lobe formation provide a foundation to analyze the cellular basis of leaf morphology and function.

cell biology↗

bioRxiv2021

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

BackgroundSchizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well characterized. MethodResting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. ResultsWe found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. ConclusionsTo our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.

neuroscience↗

bioRxiv2019

Structural brain architectures match intrinsic functional networks and vary across domains: A study from 15000+ individuals

Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of structural-functional (S-F) component maps, distributing across 9 functional domains, in both a discovery cohort (n=6005) and a replication cohort (UK Biobank, n=9214), providing a well-match multimodal spatial map template for public use. Further network module analysis suggested that unimodal cortical areas (e.g. somatomotor and visual networks) indicate higher S-F coherence, while heteromodal association cortices, especially the frontoparietal network (FPN), exhibit more S-F divergence. Collectively, these results suggest that the expanding and maturing brain association cortex demonstrates a higher degree of changes compared to unimodal cortex, which may lead to higher inter-individual variability and lower S-F coherence.

neuroscience↗

bioRxiv2019

Parallel Group ICA + ICA: Joint Estimation of Linked Functional Network Variability and Structural Covariation with Application to Schizophrenia

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called \"parallel group ICA+ICA\" that incorporates temporal fMRI information from GICA into a parallel ICA framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g. structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate inter-modality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

bioinformatics↗

bioRxiv2018

Race modifies default mode network connectivity in Alzheimer’s disease

ObjectiveTo determine if resting state functional MRI biomarkers of Alzheimers disease (AD) differ between older African Americans and Caucasians.\n\nMethodsWe analyzed MRI profiles from 78 individuals (31 African Americans, 47 Caucasians) with normal cognition (n=36) or mild cognitive impairment/mild AD dementia (MCI/AD, n=42). We compared AD-associated intra-network functional connectivity of the default mode network (DMN) according to race, and correlated domain-specific cognitive functions with functional connectivity which differed between the racial groups.\n\nResultsWe identified key differences in DMN functional connectivity associated with AD between the races. Whereas MCI/AD was associated with decreased functional connectivity within the midline core DMN subsystem in older Caucasians, MCI/AD was instead associated with increased functional connectivity within the same subsystem of older African Americans. This is despite decreased functional connectivity in the medial temporal lobe DMN subsystem in both races. Memory function was also positively associated with connectivity between the precuneus and the posterior cingulate/inferior parietal lobule within the midline core subsystem, in keeping with a less amnestic-profile in older African Americans with MCI/AD.\n\nConclusionsThese findings provide structural support that race modifies the AD phenotypes downstream from cerebral amyloid deposition, and suggests a rsf-MRI correlate of African Americans less amnestic neuropsychological profile in MCI/AD.

neuroscience↗

bioRxiv2018

Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks.

neuroscience↗