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multivariate pattern analysis: a method and software to reveal, quantify, and visualize predictive association patterns in multicollinear data

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BMC BIOINFORMATICS 2024年第1期25卷 1-21页

作者： Baumeister, Tim U. H. Aadland, Eivind Linington, Roger G. Kvalheim, Olav M. Simon Fraser Univ Dept Chem Burnaby BC Canada Western Norway Univ Appl Sci Dept Sport Food & Nat Sci Sogndal Norway Univ Bergen Dept Chem Bergen Norway

BackgroundStrongly multicollinear covariates, such as those typically represented in metabolomics applications, represent a challenge for multivariate regression analysis. These challenges are commonly circumvented by reducing the number of covariates to a subset of linearly independent variables, but this strategy may lead to loss of resolution and thus produce models with poorer interpretative potential. The aim of this work was to implement and illustrate a method, multivariate pattern analysis (MVPA), which can handle multivariate covariates without compromising resolution or model *** has been implemented in an open-source R package of the same name, mvpa. To facilitate the usage and interpretation of complex association patterns, mvpa has also been integrated into an R shiny app, mvpaShiny, which can be accessed on ***. MVPA utilizes a general projection algorithm that embraces a diversity of possible models. The method handles multicollinear and even linear dependent covariates. MVPA separates the variance in the data into orthogonal parts within the frame of a single joint model: one part describing the relations between covariates, outcome, and explanatory variables and another part describing the "net" predictive association pattern between outcome and explanatory variables. These patterns are visualized and interpreted in variance plots and plots for pattern analysis and ranking according to variable importance. Adjustment for a linear dependent covariate is performed in three steps. First, partial least squares regression with repeated Monte Carlo resampling is used to determine the number of predictive PLS components for a model relating the covariate to the outcome. Second, postprocessing of this PLS model by target projection provided a single component expressing the predictive association pattern between the outcome and the covariate. Third, the outcome and the explanatory variables were adjusted for the covariate by us

关键词： multivariate pattern analysis Multicollinear covariates Net association patterns Latent variable projection Covariate projection Target projection

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multivariate pattern analysis of cooperation and competition in constructive action

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NEUROPSYCHOLOGIA 2024年 202卷 108956页

作者： Knyazev, G. G. Savostyanov, A. N. Bocharov, A. V. Rudych, P. D. Saprigyn, A. E. Inst Neurosci & Med Timakova Str 4 Novosibirsk 630117 Russia Inst Cytol & Genet SB RAS Novosibirsk Russia

The neural underpinning of cooperative and competitive constructive activity has been investigated using massunivariate approaches. In this study, we sought to compare the results of these approaches with the results of multivariate pattern analysis (MVPA). In particular, we wanted to test whether MVPA supports the claim made in previous studies that cooperation is associated with the activity of reward-related brain circuits. Participants were required to construct a pattern on the screen either individually or in cooperation or competition with another person during an fMRI scan. Both the MVPA classification methods and the representational similarity analysis indicated the involvement of orbitofrontal and ventromedial prefrontal areas in processes that distinguish between cooperation and competition, and activation analysis showed that these areas are more active during cooperation than during competition. However, a single trial analysis showed that the effect was reversed when only winning trials were considered. In these trials, activation of reward-related areas was higher during competition than during cooperation. Moreover, the contrast between won and lost trials in terms of reward circuits involvement was sharper under competition than under cooperation. Thus, although cooperation can be generally more rewarding than competition, it is associated with smaller difference between trials lost and trials won in terms of reward circuits activation. One may speculate that in cooperation, victory and defeat are shared with the partner and, contrary to competition, are not experienced as personal achievement or failure.

关键词： Cooperation Competition fMRI multivariate pattern analysis Representational similarity analysis

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Unraveling multi-scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome-neuroimaging association study

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CNS NEUROSCIENCE & THERAPEUTICS 2024年第8期30卷 e14906页

作者： Peng, Yanmin Chai, Chao Xue, Kaizhong Tang, Jie Wang, Sijia Su, Qian Liao, Chongjian Zhao, Guoshu Wang, Shaoying Zhang, Nannan Zhang, Zhihui Lei, Minghuan Liu, Feng Liang, Meng Tianjin Med Univ Sch Med Imaging 1 Guangdong Rd Tianjin 300204 Peoples R China Tianjin Med Univ Key Lab Funct Imaging 1 Guangdong Rd Tianjin 300204 Peoples R China Tianjin Med Univ Gen Hosp Dept Radiol 154 Anshan Rd Tianjin 300052 Peoples R China Tianjin Med Univ Gen Hosp Tianjin Key Lab Funct Imaging Tianjin Peoples R China Nankai Univ Tianjin Cent Hosp 1 Sch Med Dept Radiol Tianjin Peoples R China Capital Med Univ Xuanwu Hosp Dept Radiol & Nucl Med Beijing Peoples R China Tianjin Med Univ Canc Inst & Hosp Dept Mol Imaging & Nucl Med Tianjin Peoples R China

AimsSchizophrenia is characterized by alterations in resting-state spontaneous brain activity;however, it remains uncertain whether variations at diverse spatial scales are capable of effectively distinguishing patients from healthy controls. Additionally, the genetic underpinnings of these alterations remain poorly elucidated. We aimed to address these questions in this study to gain better understanding of brain alterations and their underlying genetic factors in *** cohort of 103 individuals with diagnosed schizophrenia and 110 healthy controls underwent resting-state functional MRI scans. Spontaneous brain activity was assessed using the regional homogeneity (ReHo) metric at four spatial scales: voxel-level (Scale 1) and regional-level (Scales 2-4: 272, 53, 17 regions, respectively). For each spatial scale, multivariate pattern analysis was performed to classify schizophrenia patients from healthy controls, and a transcriptome-neuroimaging association analysis was performed to establish connections between gene expression data and ReHo alterations in *** ReHo metrics at all spatial scales effectively discriminated schizophrenia from healthy controls. Scale 2 showed the highest classification accuracy at 84.6%, followed by Scale 1 (83.1%) and Scale 3 (78.5%), while Scale 4 exhibited the lowest accuracy (74.2%). Furthermore, the transcriptome-neuroimaging association analysis showed that there were not only shared but also unique enriched biological processes across the four spatial scales. These related biological processes were mainly linked to immune responses, inflammation, synaptic signaling, ion channels, cellular development, myelination, and transporter *** study highlights the potential of multi-scale ReHo as a valuable neuroimaging biomarker in the diagnosis of schizophrenia. By elucidating the complex molecular basis underlying the ReHo alterations of this disorder, this study not only enhance

关键词： gene expression multiscale analysis multivariate pattern analysis regional homogeneity schizophrenia

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Distinct connectivity patterns in bipolar and unipolar depression: a functional connectivity multivariate pattern analysis study

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BMC NEUROSCIENCE 2024年第1期25卷 1-13页

作者： Pastrnak, Martin Klirova, Monika Bares, Martin Novak, Tomas Natl Inst Mental Hlth Klecany 25067 Czech Republic Charles Univ Prague Fac Med 3 Prague 10000 Czech Republic

BackgroundPatients with bipolar disorder (BD) and major depressive disorder (MDD) exhibit depressive episodes with similar symptoms despite having different and poorly understood underlying neurobiology, often leading to misdiagnosis and improper treatment. This exploratory study examined whole-brain functional connectivity (FC) using FC multivariate pattern analysis (fc-MVPA) to identify the FC patterns with the greatest ability to distinguish between currently depressed patients with BD type I (BD I) and those with *** a cross-sectional design, 41 BD I, 40 MDD patients and 63 control participants completed resting state functional magnetic resonance imaging scans. Data-driven fc-MVPA, as implemented in the CONN toolbox, was used to identify clusters with differential FC patterns between BD patients and MDD patients. The identified cluster was used as a seed in a post hoc seed-based analysis (SBA) to reveal associated connectivity patterns, followed by a secondary ROI-to-ROI analysis to characterize differences in connectivity between these patterns among BD I patients, MDD patients and ***-MVPA identified one cluster located in the right frontal pole (RFP). The subsequent SBA revealed greater FC between the RFP and posterior cingulate cortex (PCC) and between the RFP and the left inferior/middle temporal gyrus (LI/MTG) and lower FC between the RFP and the left precentral gyrus (LPCG), left lingual gyrus/occipital cortex (LLG/OCC) and right occipital cortex (ROCC) in MDD patients than in BD patients. Compared with the controls, ROI-to-ROI analysis revealed lower FC between the RFP and the PCC and greater FC between the RFP and the LPCG, LLG/OCC and ROCC in BD patients;in MDD patients, the analysis revealed lower FC between the RFP and the LLG/OCC and ROCC and greater FC between the RFP and the LI/*** in the RFP FC patterns between currently depressed patients with BD and those with MDD suggest potential neuroimagi

关键词： Bipolar disorder Major depressive disorder multivariate pattern analysis Functional connectivity Resting state

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A comparison of different connected-speech tasks for detecting mild cognitive impairment using multivariate pattern analysis

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APHASIOLOGY 2025年第4期39卷 476-499页

作者： Chen, Yiting Hartsuiker, Robert J. Pistono, Aurelie Univ Ghent Dept Expt Psychol Ghent Belgium

BackgroundIt is common for the elderly population to have age-associated cognitive decline and/or develop neurodegenerative diseases such as dementia. Several studies have suggested that classification algorithms based on linguistic features may be useful for the early detection of mild cognitive impairment (MCI).AimsThe current study aimed to examine connected-speech performance in people with MCI and cognitively healthy controls (HC). It tests whether patterns of lexical-semantic features extracted from these tasks could distinguish participants with MCI from HC, using univariate and multivariate *** & procedureWe selected 16 English-speaking participants with MCI and 16 matched HC from the Delaware corpus. Four connected-speech tasks (a picture description, a story narrative, a story recall, and a procedural narrative). Eight lexical-semantic features were selected for *** & resultsUnivariate analyses showed inter-group differences in revision ratio, core lexicon, or open/closed class words ratio, depending on the task. multivariate pattern analysis (MVPA) results demonstrated that the story recall task is the only task that can discriminate the two groups above *** conclude, results showed that connected-speech tasks have the potential to detect subtle language changes in people with MCI. In particular, the story recall task had the potential to predict the group of a participant (MCI or HC).

关键词： Mild cognitive impairment alzheimer's disease connected speech machine learning multivariate pattern analysis

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Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data

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NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS 2024年 164卷 105795页

作者： Marsicano, Gianluca Bertini, Caterina Ronconi, Luca Univ Bologna Dept Psychol Viale Berti Pichat 5 I-40121 Bologna Italy Univ Bologna Ctr Studies & Res Cognit Neurosci Via Rasi & Spinelli 176 I-47023 Cesena Italy Univ Vita Salute San Raffaele Sch Psychol Milan Italy IRCCS San Raffaele Sci Inst Div Neurosci Milan Italy

multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatiotemporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.

关键词： multivariate pattern analysis EEG Decoding Neurodevelopmental disorders Psychiatric Neurological conditions Autism Spectrum Disorder Schizophrenia

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Identification and effective connections of core networks in patients with temporal lobe epilepsy and cognitive impairment: Granger causality analysis and multivariate pattern analysis

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INTERNATIONAL JOURNAL OF NEUROSCIENCE 2023年第9期133卷 935-946页

作者： Jiang, Yanchun Zhang, Yanbo Nie, Liluo Liu, Huihua Zheng, Jinou Guangxi Med Univ Affiliated Hosp 1 Dept Neurol 6 Shuangyong Rd Nanning Peoples R China Nanxishan Hosp Guangxi Zhuang Autonomous Reg Dept Neurol Guilin Peoples R China Hengyang Cent Hosp Dept Neurol Hengyang Peoples R China

Objective This study aimed to explore effective connectivity (EC) of the core networks in cognition impairment associated with temporal lobe epilepsy (CI-TLE) by applying resting state and Granger causality analysis (REST-GCA). The specific brain regions that played a critical role in classification were assessed using multivariate pattern analysis (MVPA). Methods Thirty-two patients with CI-TLE and 29 healthy controls who were matched based on age and gender underwent functional magnetic resonance imaging (fMRI). Results REST-GCA revealed that patients with CI-TLE displayed decreased GC values in the following brain areas: from the posterior cingulate cortex (PCC) to the left fusiform gyrus (lFFG) and the right parahippocampal gyrus (rPPG);from the right dorsal prefrontal cortex (rDPFC) to the left superior parietal lobule (lSPL);from the left amygdala (lAG) to the PCC. Inhibitory EC was observed from the rDPFC to the PCC compared to HCs. The GC values increased from the right dorsal prefrontal cingulate cortex (rdACC) to the PCC and from the right dorsal forebrain insula (rDAI) to the right middle temporal gyrus (rMTG) in the CI-TLE patients. MVPA showed that the classification yielded an accuracy of 81.91% (78.12%, specificity =85.71%). Conclusion Our observations indicated that the abnormal EC between the frontal and parietal regions might be associated with the pathophysiological mechanism of CI-TLE. These results also indicated that EC might be play a role as a potential discriminative pattern to detect CI-TLE in patients.

关键词： cognition impairment with temporal lobe epilepsy functional magnetic resonance imaging Granger causality analysis multivariate pattern analysis

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Combined multivariate pattern analysis with frequency-dependent intrinsic brain activity to identify essential tremor

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NEUROSCIENCE LETTERS 2022年 776卷 136566页

作者： Zhang, Xiaoyu Chen, Huiyue Tao, Li Zhang, Xueyan Wang, Hansheng He, Wanlin Li, Qin Xiao, Pan Xu, Bintao Gui, Honge Lv, Fajin Luo, Tianyou Man, Yun Xiao, Zheng Fang, Weidong Chongqing Med Univ Dept Radiol Affiliated Hosp 1 1 Youyi Rd Chongqing 400016 Peoples R China Chongqing Med Univ Dept Neurol Affiliated Hosp 1 Chongqing Peoples R China

Essential tremor (ET) is the most common tremor disorder, and the intrinsic brain activity changes and diagnostic biomarkers of ET remain unclear. Combined multivariate pattern analysis (MVPA) with resting-state functional MRI (Rs-fMRI) data provides the most promising way to identify individual subjects, reveal brain activity changes, and further establish diagnostic biomarkers in neurological diseases. Using voxel-level amplitude of low-frequency fluctuations (ALFF) and local (regional homogeneity, ReHo) and global (degree centrality, DC) brain connectivity mappings based on three frequency bands (classical band: 0.01-0.10 Hz;slow-5: 0.01-0.023 Hz;slow-4: 0.023-0.073 Hz) of 162 ET patients and 153 well-matched healthy controls (HCs) as input features, MVPA (binary support vector machine, SVM) was performed to differentiate ET from HCs. Each modality achieved good classification performance, except for ReHo based on the slow-4 band with a sensitivity, specificity and total accuracy of 58.64%, 65.36%, 61.90%, respectively (P < 0.05). The classification performance with slow-4 bands was poorer than that with slow-5 and classical bands, but slow-4 bands could be used to reveal the spatial distribution changes in subcortical structures, especially the thalamus. The significant discriminative features were mostly located in the cerebello-thalamo-cortical pathway, and partial correlation analyses showed that significant discriminative features in the cerebello-thalamo-cortical pathway could be used to explain the clinical features of tremor in ET patients. Our findings revealed that voxel-level frequency-dependent ALFF, ReHo and DC could be used to discriminate ET from HCs and help to reveal intrinsic brain activity changes, further acting as potential diagnostic biomarkers.

关键词： Essential tremor Resting-state functional MRI multivariate pattern analysis Frequency-dependent Intrinsic brain activity

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A resting-state fMRI study of temporal lobe epilepsy using multivariate pattern analysis and Granger causality analysis

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JOURNAL OF NEUROIMAGING 2022年第5期32卷 977-990页

作者： Hao, Siyao Duan, Ying Qi, Lei Li, Zhimei Ren, Jiechuan Nangale, Naluyele Yang, Chunlan Beijing Univ Technol Fac Life Sci & Bioengn 100 PingLand Beijing 100124 Peoples R China Beijing Universal Med Imaging Diagnost Ctr Beijing Peoples R China Capital Med Univ Beijing Tiantan Hosp Beijing Peoples R China Sterelin Med & Diagnost Lusaka Zambia

Background and Purpose Understanding the pathogenesis of temporal lobe epilepsy (TLE) is essential for its diagnosis and treatment. The study aimed to explore regional homogeneity (ReHo) and changes in effective connectivity (EC) between brain regions in TLE patients, hoping to discover potential abnormalities in certain brain regions in TLE patients. Methods Resting-state functional magnetic resonance data were collected from 23 TLE patients and 32 normal controls (NC). ReHo was used as a feature of multivariate pattern analysis (MVPA) to explore the ability of its alterations in identifying TLE. Based on the results of the MVPA, certain brain regions were selected as seed points to further explore alterations in EC between brain regions using Granger causality analysis. Results MVPA results showed that the classification accuracy for the TLE and NC groups was 87.27%, and the right posterior cerebellum lobe, right lingual gyrus (LING_R), right cuneus (CUN_R), and left superior temporal gyrus (STG_L) provided significant contributions. Moreover, the EC from STG_L to right fusiform gyrus (FFG_R) and LING_R and the EC from CUN_R to the right occipital superior gyrus (SOG_R) and right occipital middle gyrus (MOG_R) were altered compared to the NC group. Conclusion The MVPA results indicated that ReHo abnormalities in brain regions may be an important feature in the identification of TLE. The enhanced EC from STG_L to FFG_R and LING_R indicates a shift in language processing to the right hemisphere, and the weakened EC from SOG_R and MOG_R to CUN_R may reveal an underlying mechanism of TLE.

关键词： effective connectivity Granger causality analysis multivariate pattern analysis regional homogeneity resting-state functional magnetic resonance imaging temporal lobe epilepsy

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Probabilistic Scaling of multivariate pattern analysis Reveals Cognitive State Dynamics 22

Probabilistic Scaling of Multivariate Pattern Analysis Revea...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Hughes, David H. Greenberg, Adam S. Medical College of Wisconsin Marquette University Joint Department of Biomedical Engineering United States

ISBN: (纸本)9798331520526

multivariate pattern analysis has become a popular tool in cognitive neuroscience to evaluate cognitive state dynamics. However, traditional applications of this technique evaluate the classifier predictions as a binary decision (i.e., state 1 vs. state 2). Given that cognitive state dynamics can involve complex interactions between brain networks and the presence of intrinsic neural noise, we propose that applying probabilistic scaling to classifier outputs represents a more insightful method for tracking cognitive states. Here, we present our results using attentional orienting as an example cognitive process, demonstrating that probabilistic scaling of the classifier output results in a rich classifier time course that can elucidate novel state-change dynamics. © 2025 IEEE.

关键词： attention cognitive control fMRI multivariate pattern analysis

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