scholarly journals The network underlying human higher-order motor control: Insights from machine learning-based lesion-behaviour mapping in apraxia

2019 ◽  
Author(s):  
Christoph Sperber ◽  
Daniel Wiesen ◽  
Georg Goldenberg ◽  
Hans-Otto Karnath
Keyword(s):  
Motor Control ◽  
Temporal Cortex ◽  
Neural Correlates ◽  
Brain Network ◽  
Higher Order ◽  
Support Vector ◽  
Precentral Gyrus ◽  
Canonical Correlations ◽  
Neurological Patients ◽  
Left Hemisphere Stroke

AbstractNeurological patients with apraxia of pantomime provide us with a unique opportunity to study the neural correlates of higher-order motor function. Previous studies using lesion-behaviour mapping methods led to inconsistent anatomical results, reporting various lesion locations to induce this symptom. We hypothesised that the inconsistencies might arise from limitations of mass-univariate lesion-behaviour mapping approaches if our ability to pantomime the use of objects is organised in a brain network. Thus, we investigated apraxia of pantomime by using multivariate lesion behaviour mapping based both on support vector regression and sparse canonical correlations in a sample of 130 left-hemisphere stroke patients. Both multivariate methods identified multiple areas to underlie high-order motor control, including inferior parietal lobule, precentral gyrus, posterior parts of middle temporal cortex, and insula. Further, long association fibres were affected, such as the superior longitudinal fascicle, inferior occipito-frontal fascicle, uncinated fascicle, and superior occipito-frontal fascicle. The findings thus not only underline the benefits of multivariate lesion-behaviour mapping in brain networks, but they also uncovered that higher-order motor control indeed is based on a common anatomical network.

scholarly journals Mapping the human praxis network: an investigation of white matter disconnection in limb apraxia

2020 ◽  
Author(s):  
Hannah Rosenzopf ◽  
Daniel Wiesen ◽  
Alexandra Basilakos ◽  
Grigori Yourganov ◽  
Leonardo Bonilha ◽  
...  
Keyword(s):  
White Matter ◽  
Left Hemisphere ◽  
Structural Damage ◽  
Parietal Lobe ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Support Vector ◽  
Precentral Gyrus ◽  
Support Vector Regression Model ◽  
Limb Apraxia

AbstractStroke to the left hemisphere of the brain can cause limb apraxia, a disorder characterised by deficits of higher order motor skills such as the failure to accurately produce meaningful gestures. This disorder provides unique insights into the anatomy of the human praxis system. The present study aimed to identify the structural brain network, that when damaged by stroke, causes limb apraxia. We assessed the ability to perform meaningful gestures with the hand in 101 patients with chronic left hemisphere stroke. Structural damage to white matter fibres was assessed by diffusion tensor imaging. A support vector regression model predicting apraxia based on individual topographies of tract-based fractional anisotropy was utilised to obtain multivariate topographical inference. We found pathological white matter alterations in a densely connected fronto-temporo-parietal network of short and long association fibres to predict limb apraxia deficits. Major disconnection affected temporo-parietal and temporo-temporal connections. Grey matter areas with a high number of disconnections included inferior parietal lobe, middle and superior temporal gyrus, inferior and middle frontal lobe, precentral gyrus, putamen, and caudate nucleus. These results demonstrate the relevance of frontal and inferior parietal regions in praxis, but they also highlight the temporal lobe and its connections to be an important contributor to the human praxis network.

scholarly journals Evaluating fMRI-Based Estimation of Eye Gaze During Naturalistic Viewing

2019 ◽  
Vol 30 (3) ◽  
pp. 1171-1184 ◽  
Author(s):  
Jake Son ◽  
Lei Ai ◽  
Ryan Lim ◽  
Ting Xu ◽  
Stanley Colcombe ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Eye Gaze ◽  
Area Under The Curve ◽  
Neural Correlates ◽  
Brain Network ◽  
Support Vector ◽  
Angular Deviation ◽  
Eye Fixations ◽  
Gaze Patterns ◽  
Primary Determinant

Abstract The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye’s orbit using a 1.5-min calibration scan. Here, we provide confirmatory validation of the PEER method’s ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2°). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n = 448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of the two movies is being watched based on the predicted eye gaze patterns (area under the curve = 0.90 ± 0.02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

scholarly journals Evaluating fMRI-Based Estimation of Eye Gaze during Naturalistic Viewing

2018 ◽  
Author(s):  
Jake Son ◽  
Lei Ai ◽  
Ryan Lim ◽  
Ting Xu ◽  
Stanley Colcombe ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Eye Gaze ◽  
Area Under The Curve ◽  
Neural Correlates ◽  
Brain Network ◽  
Support Vector ◽  
Angular Deviation ◽  
Eye Fixations ◽  
Gaze Patterns ◽  
Primary Determinant

ABSTRACTThe collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye-tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye’s orbit using a 1.5-minute calibration scan. Here, we provide confirmatory validation of the PEER method’s ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2°). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n=448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of two movies is being watched based on the predicted eye gaze patterns (area under the curve = .90 ± .02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

Higher-Order Visual Cortex Shows Stronger Neural Correlates of Visual and Multisensory Detection Behavior Compared to Primary Visual Cortex

2019 ◽  
Author(s):  
Guido Meijer ◽  
Pietro Marchesi ◽  
Jorge Mejias ◽  
Jorrit Montijn ◽  
Carien Lansink ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neural Correlates ◽  
Higher Order

scholarly journals Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory

2021 ◽  
Vol 11 (1) ◽  
pp. 111
Author(s):  
Farzad V. Farahani ◽  
Magdalena Fafrowicz ◽  
Waldemar Karwowski ◽  
Bartosz Bohaterewicz ◽  
Anna Maria Sobczak ◽  
...  
Keyword(s):  
Resting State ◽  
Network Architecture ◽  
Inferior Frontal Gyrus ◽  
Brain Network ◽  
Time Of Day ◽  
Precentral Gyrus ◽  
Network Organization ◽  
Diurnal Variability ◽  
Sleep Inertia ◽  
Brain Functions

Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks.

scholarly journals Structural brain network topology underpinning ADHD and response to methylphenidate treatment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristi R. Griffiths ◽  
Taylor A. Braund ◽  
Michael R. Kohn ◽  
Simon Clarke ◽  
Leanne M. Williams ◽  
...  
Keyword(s):  
Brain Network ◽  
Global Network ◽  
Support Vector ◽  
Structural Topology ◽  
Local Efficiency ◽  
Structural Brain Network ◽  
Structural Architecture ◽  
The Right ◽  
Tractography Data ◽  
Treatment Prognosis

AbstractBehavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.

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