Biology subjects

Khan, A. R.

Publications and source records attributed to Khan, A. R..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Cortical Network Disruption is Minimal in Early Stages of Psychosis

1Background and HypothesisChronic schizophrenia is associated with white matter disruption and topological reorganization of cortical connectivity but the trajectory of these changes over the disease course are poorly understood. Current white matter studies in first-episode psychosis (FEP) patients using diffusion magnetic resonance imaging (dMRI) suggest such disruption may be detectable at the onset of psychosis, but specific results vary widely and few reports have contextualized their findings with direct comparison to chronic patients. Here, we test the hypothesis that structural changes are not a significant feature of early psychosis. Study DesignDiffusion and T1-weighted 7T MR scans were obtained from N=113 (61 FEP patients, 37 controls, 15 chronic patients) recruited from an established cohort in London, Ontario. Voxel- and network-based analyses were used to detect changes in diffusion microstructural parameters. Graph theory metrics were used to probe changes in the cortical network hierarchy and to assess the vulnerability of hub regions to disruption. Experiments were replicated with N=167 (111 patients, 56 controls) from the Human Connectome Project - Early Psychosis (HCP-EP) dataset. Study ResultsWidespread microstructural changes were found in chronic patients, but changes in FEP patients were minimal. Unlike chronic patients, no appreciable topological changes in the cortical network were observed in FEP patients. These results were replicated in the early psychosis patients of the HCP-EP datasets, which were indistinguishable from controls on nearly all metrics. ConclusionsThe white matter structural changes observed in chronic schizophrenia are not a prominent feature in the early stages of this illness.

neuroscience↗

bioRxiv2023

Focal acetylcholinergic modulation of the human midcingulo-insular network during attention: Meta-analytic neuroimaging and behavioral evidence

The basal forebrain cholinergic neurons provide acetylcholine to the cortex via large projections. Recent molecular imaging work in humans indicates that the cortical cholinergic innervation is not uniformly distributed, but rather may disproportionately innervate cortical areas relevant to supervisory attention. In this study, we therefore reexamined the spatial relationship between acetylcholinergic modulation and attention in the human cortex using meta-analytic strategies targeting both pharmacological and nonpharmacological neuroimaging studies. We found that pharmaco-modulation of acetylcholine evoked both increased activity in the anterior cingulate and decreased activity in the opercular and insular cortex. In large independent meta-analyses of non-pharmacological neuroimaging research, we demonstrate that during attentional engagement these cortical areas exhibit (1) task-related co-activation with the basal forebrain, (2) task-related co-activation with one another, and (3) spatial overlap with dense cholinergic innervations originating from the BF, as estimated by multimodal PET and MR imaging. Finally, we provide meta-analytic evidence that pharmaco-modulation of acetylcholine also induces a speeding of responses to targets with no apparent tradeoff in accuracy. In sum, we demonstrate in humans that acetylcholinergic modulation of midcingulo-insular hubs of the ventral attention/salience network via basal forebrain afferents may coordinate selection of task relevant information, thereby facilitating cognition and behavior.

neuroscience↗

bioRxiv2023

Novel insights into hippocampal perfusion using high-resolution, multi-modal 7T MRI

We present a comprehensive study on the non-invasive measurement of hippocampal perfusion. Using high-resolution 7 Tesla arterial spin labelling data, we generated robust perfusion maps and observed significant variations in perfusion among hippocampal subfields, with CA1 exhibiting the lowest perfusion levels. Notably, these perfusion differences were robust and detectable even within five minutes and just fifty perfusion-weighted images per subject. To understand the underlying factors, we examined the influence of image quality metrics, various tissue microstructure and morphometry properties, macrovasculature and cytoarchitecture. We observed higher perfusion in regions located closer to arteries, demonstrating the influence of vascular proximity on hippocampal perfusion. Moreover, ex vivo cytoarchitectonic features based on neuronal density differences appeared to correlate stronger with hippocampal perfusion than morphometric measures like gray matter thickness. These findings emphasize the interplay between microvasculature, macrovasculature, and metabolic demand in shaping hippocampal perfusion. Our study expands the current understanding of hippocampal physiology and its relevance to neurological disorders. By providing in vivo evidence of perfusion differences between hippocampal subfields, our findings have implications for diagnosis and potential therapeutic interventions. In conclusion, our study provides a valuable resource for extensively characterising hippocampal perfusion.

neuroscience↗

bioRxiv2023

A population-averaged structural connectomic brain atlas of 422 HCP-Aging subjects

Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modelling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration. The dataset described in this article contains a set of multi-contrast structural connectomic MRI atlases, including T1w, T2w, and FOD templates, along with the associated whole brain tractograms. The templates were made using multi-contrast group-wise registration based on 3T MRIs of 422 Human Connectome Project in Aging (HCP-A) subjects. To enhance the usability, probabilistic tissue maps and segmentation of 22 subcortical structures are provided. Finally, the subthalamic nucleus shown in the atlas is parcellated into sensorimotor, limbic, and associative sub-regions based on their structural connectivity to facilitate the analysis and planning of deep brain stimulation procedures. The dataset is available on the OSF Repository: https://osf.io/p7syt. Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@59fb82org.highwire.dtl.DTLVardef@18f1f44org.highwire.dtl.DTLVardef@14de18dorg.highwire.dtl.DTLVardef@1318a8org.highwire.dtl.DTLVardef@1b7bea8_HPS_FORMAT_FIGEXP M_TBL C_TBL Value of the dataO_LIThese publicly available templates were created using 422 HCP-Aging subjects, representing averaged anatomical and structural connectivity features of the healthy aging brain. C_LIO_LIMatching T1w, T2w, and fiber orientation distribution (FOD) templates are provided, together with the associated tractograms that contain 20K and 2 million streamlines. C_LIO_LISegmentation of 22 subcortical structures and probabilistic tissue maps are provided to enhance the usability of the templates. C_LIO_LIParcellation of the subthalamic nucleus, based on structural connectivity of the included cohort at 0.3 x 0.3 x 0.3 mm3 resolution, is provided. C_LI

bioengineering↗

bioRxiv2023

Multimodal gradients of human basal forebrain connectivity

The cholinergic innervation of the cortex originates almost entirely from populations of neurons in the basal forebrain (BF). Structurally, the ascending BF cholinergic projections are highly branched, with individual cells targeting multiple different cortical regions. However, it is not known whether the structural organization of basal forebrain projections reflects their functional integration with the cortex. We therefore used high-resolution 7T diffusion and resting state functional MRI in humans to examine multimodal gradients of BF cholinergic connectivity with the cortex. Moving from anteromedial to posterolateral BF, we observed reduced tethering between structural and functional connectivity gradients, with the most pronounced dissimilarity localized in the nucleus basalis of Meynert (NbM). The cortical expression of this structure-function gradient revealed progressively weaker tethering moving from unimodal to transmodal cortex, with the lowest tethering in midcingulo-insular cortex. We used human [18F] fluoroethoxy-benzovesamicol (FEOBV) PET to demonstrate that cortical areas with higher concentrations of cholinergic innervation tend to exhibit lower tethering between BF structural and functional connectivity, suggesting a pattern of increasingly diffuse axonal arborization. Anterograde viral tracing of cholinergic projections and [18F] FEOBV PET in mice confirmed a gradient of axonal arborization across individual BF cholinergic neurons. Like humans, cholinergic neurons with the highest arborization project to cingulo-insular areas of the mouse isocortex. Altogether, our findings reveal that BF cholinergic neurons vary in their branch complexity, with certain subpopulations exhibiting greater modularity and others greater diffusivity in the functional integration of their cortical targets.

neuroscience↗

bioRxiv2023

Determining epitope specificity of T-cell receptors with transformers

MotivationT-cell receptors (TCR) on T cells recognize and bind to epitopes presented by the major histocompatibility complex (MHC) in case of an infection or cancer. However, the high diversity of TCRs, as well as their unique and complex binding mechanisms underlying epitope recognition, make it difficult to predict the binding between TCR and epitope. Here, we present the utility of transformers, a deep learning strategy that incorporates an attention mechanism that learns the informative features, and show that these models pretrained on a large set of protein sequences outperform current strategies. MethodWe compared three pre-trained auto-encoder transformer models (ProtBERT, ProtAlbert, ProtElectra) and one pre-trained auto-regressive transformer model (ProtXLNet) to predict the binding specificity of TCRs to 25 epitopes from the VDJdb database (human and murine). Two additional modifications were performed to incorporate gene usage of the TCRs in the four transformer models. ResultsOf all 12 transformer implementations (4 models with 3 different modifications), a modified version of the ProtXLNet model could predict TCR-epitope pairs with the highest accuracy (weighted F1 score 0.55 simultaneously considering all 25 epitopes). The modification included additional features representing the gene names for the TCRs. We also showed that the basic implementation of transformers outperformed the previously available methods, i.e. TCRGP, TCRDist and DeepTCR, developed for the same biological problem, especially for the hard-to-classify labels. ConclusionWe show that the proficiency of transformers in attention learning can indeed be made operational in a complex biological setting like TCR binding prediction. Further ingenuity in utilizing the full potential of transformers, either through attention head visualization or introducing additional features, can further extend T-cell research avenues. AvailabilityData and code are available on https://github.com/InduKhatri/tcrformer

bioinformatics↗

bioRxiv2023

Evaluation of surface-based hippocampal registration using ground-truth subfield definitions

The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches. HighlightsO_LIHippocampal subfields contain microcircuits that are critical for memory and vulnerable to neurological disease. C_LIO_LIHippocampi have variable folding patterns between individuals, making them hard to register or parcellate. C_LIO_LIWe present a surface-based hippocampal registration method that is analogous to neocortical inflation to a sphere and registration. C_LIO_LITesting in seven detailed 3D histology samples revealed successful registration with respect to hippocampal subfields, and outperformed more conventional methods. C_LIO_LIThis method provides groundwork for detailed multimodal hippocampal mapping across subjects and datasets in the future. C_LI Data availabilityThe methodological advancements described here are made easily accessible in the latest version of open source software HippUnfold1. Code used in the development and testing of these methods, as well as preprocessed images, manual segmentations, and results, are openly available2.

neuroscience↗

bioRxiv2022

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging templates and datasets. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (n = 132 subjects for a total of 15,232 fiducials) and more than 10 brain templates (4,288 fiducials), compiling over 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1-2 mm (using a total of 50,336 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure (BIDS) allowing for facile incorporation into modern neuroimaging analysis pipelines. Data is accessible on GitHub (https://github.com/afids/afids-data).

neuroscience↗

bioRxiv2022

An automated BIDS-App for brain segmentation of human fetal functional MRI data

Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics [≥]0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.

neuroscience↗

bioRxiv2022

Investigating the in vivo Spatial Distribution of Hippocampal Microstructure and Macrostructure

The hippocampus is classically divided into mesoscopic subfields which contain varying microstructure that contribute to their unique functional roles. It has been challenging to characterize this microstructure with current MR based neuroimaging techniques. In this work, we used diffusion MRI and a novel surface-based approach in the hippocampus which revealed distinct microstructural distributions of neurite density and dispersion, T1w/T2w ratio as a proxy for myelin content, fractional anisotropy, and mean diffusivity. We used the Neurite Orientation Dispersion and Density Imaging (NODDI) model optimized for gray matter diffusivity to characterize neurite density and dispersion. We found that neurite dispersion was highest in the Cornu Ammonis (CA) 1 and subiculum subfields which likely captures the large heterogeneity of tangential and radial fibers, such as the Schaffer collaterals, perforant path, and pyramidal neurons. Neurite density and T1w/T2w were highest in the subiculum and CA3 and lowest in CA1, which may reflect known myeloarchitecture differences between these subfields. Using a simple logistic regression model, we showed that neurite density, dispersion, and T1w/T2w measures provided good separability across the subfields, suggesting that they may be sensitive to the known variability in subfield cyto- and myeloarchitecture. We report macrostructural measures of gyrification, thickness, and curvature that were in line with ex vivo descriptions of hippocampal anatomy. We employed a multivariate orthogonal projective non-negative matrix factorization (OPNNMF) approach to capture co-varying regions of macro- and microstructure across the hippocampus. The clusters were highly variable along the medial-lateral (proximal-distal) direction, likely reflecting known differences in morphology, cytoarchitectonic profiles, and connectivity. Finally, we show that by examining the main direction of diffusion relative to canonical hippocampal axes, we could identify regions with stereotyped microstructural orientations that may map onto specific fiber pathways, such as the Schaffer collaterals, perforant path, fimbria, and alveus. These results highlight the value of combining in vivo diffusion MRI with computational approaches for capturing hippocampal microstructure, which may provide useful features for understanding cognition and for diagnosis of disease states.

neuroscience↗

bioRxiv2022

Detection of local growth patterns in longitudinally imaged low-grade gliomas

BackgroundDiffuse low-grade gliomas (LGGs) are primary brain tumors with infiltrative, anisotropic growth related to surrounding white and grey matter structures. In this study, we illustrate the use of deformation-based morphometry (DBM) as a simple and objective method to study the local change in growth patterns of LGGs. MethodsAn imaging pipeline was developed involving the creation of patient-specific average templates and nonlinear registration of pre-treatment follow-up MRIs to the average template. Jacobian maps were derived and analyzed to identify areas of tissue expansion and contraction over time. ResultsOur analysis demonstrates that tissue expansion occurs primarily around the edges of the tumor, while the lesion core and areas adjacent to obstacles, such as the skull, show no significant growth. Tumors also appeared to grow faster and predominantly in areas of white matter. Regions of the brain surrounding the lesion showed slight contraction over time, likely representing compression due to mass effect of the tumor. ConclusionsWe demonstrate that DBM is a useful clinical tool to understand the long-term clinical course of an individuals tumor and identify areas of rapid growth, which can explain the clinical signs and symptoms, predict future symptoms, and guide targeted diagnostics and therapy. HighlightsO_LILow-grade glioma expansion occurs primarily around the edges of the tumor. C_LIO_LITumor cores and tissue next to obstacles show no significant growth over time. C_LIO_LIDBM provides a clinically valuable assessment of local tumor growth and activity. C_LI

cancer biology↗

bioRxiv2021

Enabling complex fibre geometries using 3D printed axon-mimetic phantoms

PurposeTo introduce a method to create 3D-printed axon-mimetic phantoms with complex fibre orientations to characterize the performance of diffusion MRI models and representations in the presence of orientation dispersion. MethodsAn extension to an open source 3D printing package was created to 3D print a set of five 3D-printed axon-mimetic (3AM) phantoms with various combinations of bending and crossing fibre orientations. A two-shell diffusion MRI scan of the five phantoms in water was performed at 9.4T. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), the ball and stick model, neurite orientation density and dispersion imaging (NODDI), and Bingham-NODDI were all fit to the resulting diffusion MRI data. A fiducial in each phantom was used to register a ground truth map of that phantoms crossing angles and/or arc radius to the diffusion-weighted images. Metrics from each model and representation were compared to the ground-truth maps, and a quadratic regression model was fit to each combination of output metric and ground-truth metric. ResultsThe mean diffusivity (MD) metric defined by DTI was insensitive to crossing angle, but increased with fibre curvature. Axial diffusivity (AD) decreased sharply with increasing crossing angle. DKIs diffusivity metrics replicated the trends seen in DTI, and its mean kurtosis (MK) metric, decreased with fibre curvature, except in regions with high crossing angles. The estimated stick volume fraction in the ball and stick model decreased with increasing fibre curvature and crossing angle. NODDIs intra-neurite volume fraction was insensitive to crossing angle, and its orientation dispersion index (ODI) was strongly correlated to crossing angle. Bingham-NODDIs intra-neurite volume fraction was also insensitive to crossing angle, while its primary ODI (ODIP) was also strongly correlated to crossing angle and its secondary ODI (ODIS) was insensitive to crossing angle. For both NODDI models, the volume fractions of the extra-neurite and CSF compartments had low reliability with no clear relationship to crossing angle. ConclusionsThis study demonstrates that inexpensive 3D-printed axon-mimetic phantoms can be used to investigate the effect of fibre curvature and crossings on diffusion MRI representations and models of diffusion signal. As a proof of concept, the dependence of several representations and models on fibre dispersion/crossing were investigated. As expected, Bingham-NODDI was best able to characterize planar fibre dispersion in the phantoms.

neuroscience↗

bioRxiv2021

HippUnfold: Automated hippocampal unfolding, morphometry, and subfield segmentation

Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subjects hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.

neuroscience↗

bioRxiv2021

The Role of the Temporal Pole in Temporal Lobe Epilepsy: A Diffusion Kurtosis Imaging Study

ObjectiveThis study aims to evaluate the use of diffusion kurtosis imaging (DKI) to detect microstructural abnormalities within the temporal pole (TP) in temporal lobe epilepsy (TLE) patients. MethodsDKI quantitative maps were obtained from fourteen lesional (MRI+) and ten non-lesional (MRI-) TLE patients, along with twenty-one healthy controls. This included mean (MK); radial (RK) and axial kurtosis (AK); mean diffusivity (MD) and axonal water fraction (AWF). Automated fiber quantification (AFQ) was used to quantify DKI measurements along the inferior longitudinal (ILF) and uncinate fasciculus (Unc). ILF and Unc tract profiles were compared between groups and tested for correlation with seizure duration. To characterize temporopolar cortex (TC) microstructure, DKI maps were sampled at varying depths from superficial white matter (WM) towards the pial surface. Each patient group was separated according to side ipsilateral to the epileptogenic temporal lobe and their AFQ results were used as input for statistical analyses. ResultsSignificant differences were observed between MRI+ and controls (p < 0.005), towards the most anterior of ILF and Unc proximal to the TP of the left (not right) ipsilateral temporal lobe for MK, RK, AWK and MD. Noticeable differences were also observed mostly towards the TP for MK, RK and AWK in the MRI-group. DKI measurements correlated with seizure duration, mostly towards the anterior segments of the WM bundles. Stronger differences in MK, RK and AWF within the TC were observed in the MRI+ and noticeable differences (except for MD) in MRI-groups compared to controls. SignificanceThe study demonstrates that DKI has potential to detect subtle microstructural alterations within the anterior segments of the ILF and Unc and the connected TC in TLE patients including MRI-subjects. This could aid our understanding of the extrahippocampal areas involved in seizure generation in TLE and might inform surgical planning, leading to better seizure outcomes.

neuroscience↗

bioRxiv2021

Structural basis for the specificity of PPM1H phosphatase for Rab GTPases

LRRK2 serine/threonine kinase is associated with inherited Parkinsons disease. LRRK2 phosphorylates a subset of Rab GTPases within their switch 2 motif to control their interactions with effectors. Recent work has shown that the metal-dependent protein phosphatase PPM1H counteracts LRRK2 by dephosphorylating Rabs. PPM1H is highly selective for LRRK2 phosphorylated Rabs, and closely related PPM1J exhibits no activity toward substrates such as Rab8a phosphorylated at Thr72 (pThr72). Here we have identified the structural determinant of PPM1H specificity for Rabs. The crystal structure of PPM1H reveals a structurally conserved phosphatase fold that strikingly has evolved a 110-residue flap domain adjacent to the active site. The flap domain distantly resembles tudor domains that interact with histones in the context of epigenetics. Cellular assays, crosslinking and 3-D modelling suggest that the flap domain encodes the docking motif for phosphorylated Rabs. Consistent with this hypothesis, a PPM1J chimera with the PPM1H flap domain dephosphorylates pThr72 of Rab8a both in vitro and in cellular assays. Therefore, PPM1H has acquired a Rab-specific interaction domain within a conserved phosphatase fold.

biochemistry↗

bioRxiv2021

Characterizing white matter alterations in drug-naïve de novo Parkinsons disease with diffusion MRI

Parkinsons disease (PD) is a progressive neurodegenerative disorder that is characterized by a range of motor and non-motor symptoms, often with the motor dysfunction initiated unilaterally. Knowledge regarding disease-related alterations in white matter pathways can effectively help improve the understanding of the disease and propose targeted treatment strategies. Microstructural imaging techniques, including diffusion tensor imaging (DTI), allows inspection of white matter integrity to study the pathogenesis of various neurological conditions. Previous voxel-based analyses with DTI measures, such as fractional anisotropy and mean diffusivity have uncovered changes in brain regions that are associated with PD, but the conclusions were inconsistent, partially due to small patient cohorts and the lack of consideration for clinical laterality onset, particularly in early PD. Fixel-based analysis (FBA) is a recent framework that offers tract-specific insights regarding white matter health, but very few FBA studies on PD exist. We present a study that reveals strengthened and weakened white matter integrity that is subject to symptom laterality in a large drug-naive de novo PD cohort using complementary DTI and FBA measures. The findings suggest that the disease gives rise to both functional degeneration and the creation of compensatory networks in the early stage.

neuroscience↗

bioRxiv2021

Activity in perirhinal and entorhinal cortex predicts observer-specific perceived visual similarities between objects

Vision neuroscience has made great strides in understanding the hierarchical organization of object representations along the ventral visual stream (VVS). How VVS representations capture fine-grained visual similarities between objects that observers subjectively perceive has received limited examination so far. In the current study, we addressed this question by focusing on perceived visual similarities among subordinate exemplars of real world-categories. We hypothesized that these perceived similarities are reflected with highest fidelity in neural activity patterns downstream from inferotemporal regions, namely in perirhinal and anterolateral entorhinal cortex in the medial temporal-lobe. To address this issue with fMRI, we administered a modified 1-Back task that required discrimination between category exemplars as well as categorization. Further, we obtained observer-specific ratings of perceived visual similarities, which predicted behavioural performance during scanning. As anticipated, we found that activity patterns in perirhinal and anterolateral entorhinal cortex predicted the structure of perceived visual similarity relationships among category exemplars, including its observer-specific component, with higher precision than any other VVS region. Our findings provide new evidence that subjective aspects of object perception that rely on fine-grained visual differentiation are reflected with highest fidelity in the medial temporal lobe.

neuroscience↗

bioRxiv2020

Application of the Anatomical Fiducials Framework to a Clinical Dataset of Patients with Parkinsons Disease

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducials (AFIDs) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinsons Disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.

neuroscience↗