Multivoxel Pattern Analysis for fMRI Data: A Review

Mahmoudi, Abdelhak; Takerkart, Sylvain; Regragui, Fakhita; Boussaoud, Driss; Brovelli, Andrea

doi:10.1155/2012/961257

Cited by 146 publications

(89 citation statements)

References 57 publications

(61 reference statements)

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“…Multivoxel pattern analysis (MVPA) uses machine learning, a branch of artificial intelligence, to automatically find multivariate patterns of brain structure and/or function that accurately predict categorical or continuous variables (Mahmoudi, et al, 2012;Orru, et al, 2012). MVPA is a highly sensitive method due to its ability to utilize subtle signals across voxels that tend to be undetectable by univariate analyses (Kamitani and Tong, 2005).…”

Section: Measuring Dmnmentioning

confidence: 99%

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Default mode network as a potential biomarker of chemotherapy-related brain injury

Kesler

2014

Neurobiology of Aging

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Chronic medical conditions and/or their treatments may interact with aging to alter or even accelerate brain senescence. Adult onset cancer, for example, is a disease associated with advanced aging and emerging evidence suggests a profile of subtle but diffuse brain injury following cancer chemotherapy. Breast cancer is currently the primary model for studying these “chemobrain” effects. Given the widespread changes to brain structure and function as well as the common impairment of integrated cognitive skills observed following breast cancer chemotherapy, it is likely that large-scale brain networks are involved. Default mode network (DMN) is a strong candidate considering its preferential vulnerability to aging and sensitivity to toxicity and disease states. Additionally, chemotherapy is associated with several physiologic effects including increased inflammation and oxidative stress that are believed to elevate toxicity in the DMN. Biomarkers of DMN connectivity could aid in the development of treatments for chemotherapy-related cognitive decline. For example, certain nutritional interventions could potentially reduce the metabolic changes (e.g. amyloid beta toxicity) associated with DMN disruption.

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Section: Measuring Dmnmentioning

confidence: 99%

“…Linear support vector machine is currently the most common MVPA approach because of its ability to handle large, high-dimensional datasets (Orru, et al, 2012). Like all of the above methods, region of interest, or feature selection, can significantly influence MVPA results (Mahmoudi, et al, 2012). …”

Section: Measuring Dmnmentioning

confidence: 99%

Default mode network as a potential biomarker of chemotherapy-related brain injury

Kesler

2014

Neurobiology of Aging

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“…The recollective experience is personal and subjective, and thus historically, it has been difficult to capture with objective measures. However, the recent application of multivoxel pattern analysis (MVPA; Haxby, 2012;Mahmoudi, Takerkart, Regragui, Boussaoud, & Brovelli, 2012;Tong & Pratte, 2012;Haynes & Rees, 2006;Norman, Polyn, Detre, & Haxby, 2006) to functional brain imaging data provides a way of examining recollection from an objective standpoint. MVPA can quantify cortical reinstatement or reactivation (Rissman & Wagner, 2012;Danker & Anderson, 2010;Rugg, Johnson, Park, & Uncapher, 2008), the phenomenon by which stimulusspecific patterns of brain activation elicited at perception are reactivated during subsequent memory retrieval.…”

Section: Introductionmentioning

confidence: 99%

Distributed Patterns of Reactivation Predict Vividness of Recollection

St-Laurent

Abdi

Buchsbaum

2015

Journal of Cognitive Neuroscience

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Abstract■ According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection. ■

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“…Using general linear models, these methods treat covariance across neighboring voxels as noise, rather than potentially informative signal. Such covariance is normally reduced by spatial smoothing (Mahmoudi et al 2012). Multivoxel pattern analysis (MVPA) avoids this signal loss issue without involving spatial smoothing.…”

Section: Introductionmentioning

confidence: 99%

Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches

Zhong

Chen

Nantes

et al. 2016

Brain Imaging and Behavior

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A structural or functional pattern of neuroplasticity that could systematically discriminate between people with impaired and preserved motor performance could help us to understand the brain networks contributing to preservation or compensation of behavior in multiple sclerosis (MS). This study aimed to (1) investigate whether a machine learning-based technique could accurately classify MS participants into groups defined by upper extremity function (i.e. motor function preserved (MP) vs. motor function impaired (MI)) based on their regional grey matter measures (GMM, cortical thickness and deep grey matter volume) and inter-regional functional connection (FC), (2) investigate which features (GMM, FC, or GMM + FC) could classify groups more accurately, and (3) identify the multivariate patterns of GMM and FCs that are most discriminative between MP and MI participants, and between each of these groups and the healthy controls (HCs). With 26 MP, 25 MI, and 21 HCs (age and sex matched) underwent T1-weighted and resting-state functional MRI at 3 T, we applied support vector machine (SVM) based classification to learn discriminant functions indicating regions in which GMM or between which FCs were most discriminative between groups. This study demonstrates that there exist structural and FC patterns sufficient for correct classification of upper limb motor ability of people with MS. The classifier with GMM + FC features yielded the highest accuracy of 85.61 % (p < 0.001) to distinguish between the MS groups using leave-one-out cross-validation. It suggests that a machine-learning approach combining structural and functional features is useful for identifying the specific neural substrates that are necessary and sufficient to preserve motor function among people with MS.

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Multivoxel Pattern Analysis for fMRI Data: A Review

Cited by 146 publications

References 57 publications

Default mode network as a potential biomarker of chemotherapy-related brain injury

Default mode network as a potential biomarker of chemotherapy-related brain injury

Distributed Patterns of Reactivation Predict Vividness of Recollection

Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches

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