Multivariate Pattern Analysis Research Articles

Advancements in Artificial Intelligence and Data Science: Models, Applications, and Challenges

The text that follows gives an overview of the numerous uses of artificial intelligence (AI) and data science in a variety of fields, focusing on their potential advantages and drawbacks. Healthcare, finance, education, smart cities, and the development of disease therapies have all showed promise for AI. The study examines several AI models, such as ontological, statistical, hybrid, and biological models, in addition to the restrictions placed by deep learning techniques. The study on brain architecture and function is also included in the article, along with applications of multivariate pattern analysis, bioinspired systems, and machine learning to the analysis and processing of enormous datasets. The literature study emphasizes how critical it is to address societal and ethical issues with AI.

Decreased intrinsic neural timescale in treatment-naïve adolescent depression

BackgroundMajor depressive disorder (MDD) is mainly characterized by its core dysfunction in higher-order brain cortices involved in emotional and cognitive processes, whose neurobiological basis remains unclear. In this study, we applied a relatively new developed resting-state functional magnetic resonance imaging (rs-fMRI) method of intrinsic neural timescale (INT), which reflects how long neural information is stored in a local brain area and reflects an ability of information integration, to investigate the local intrinsic neural dynamics using univariate and multivariate analyses in adolescent depression. MethodBased on the rs-fMRI data of sixty-six treatment-naïve adolescents with MDD and fifty-two well-matched healthy controls (HCs), we calculated an INT by assessing the magnitude of autocorrelation of the resting-state brain activity, and then compared the difference of INT between the two groups. Correlation between abnormal INT and clinical features was performed. We also utilized multivariate pattern analysis to determine whether INT could differentiate MDD patients from HCs at the individual level. ResultCompared with HCs, patients with MDD showed shorter INT widely distributed in cortical and partial subcortical regions. Interestingly, the decreased INT in the left hippocampus was related to disease severity of MDD. Furthermore, INT can distinguish MDD patients from HCs with the most discriminative regions located in the dorsolateral prefrontal cortex, angular, middle occipital gyrus, and cerebellar posterior lobe. ConclusionOur research aids in advancing understanding the brain abnormalities of treatment-naïve adolescents with MDD from the perspective of the local neural dynamics, highlighting the significant role of INT in understanding neurophysiological mechanisms. This study shows that the altered intrinsic timescales of local neural signals widely distributed in higher-order brain cortices regions may be the neurodynamic basis of cognitive and emotional disturbances in MDD patients, and provides preliminary support for the suggestion that these could be used to aid the identification of MDD patients in clinical practice.

Scene-selectivity in CA1/subicular complex: Multivoxel pattern analysis at 7T

Prior univariate functional magnetic resonance imaging (fMRI) studies in humans suggest that the anteromedial subicular complex of the hippocampus is a hub for scene-based cognition. However, it is possible that univariate approaches were not sufficiently sensitive to detect scene-related activity in other subfields that have been implicated in spatial processing (e.g., CA1). Further, as connectivity-based functional gradients in the hippocampus do not respect classical subfield boundary definitions, category selectivity may be distributed across anatomical subfields. Region-of-interest approaches, therefore, may limit our ability to observe category selectivity across discrete subfield boundaries. To address these issues, we applied searchlight multivariate pattern analysis to 7T fMRI data of healthy adults who undertook a simultaneous visual odd-one-out discrimination task for scene and non-scene (including face) visual stimuli, hypothesising that scene classification would be possible in multiple hippocampal regions within, but not constrained to, anteromedial subicular complex and CA1. Indeed, we found that the scene-selective searchlight map overlapped not only with anteromedial subicular complex (distal subiculum, pre/para subiculum), but also inferior CA1, alongside posteromedial (including retrosplenial) and parahippocampal cortices. Probabilistic overlap maps revealed gradients of scene category selectivity, with the strongest overlap located in the medial hippocampus, converging with searchlight findings. This was contrasted with gradients of face category selectivity, which had stronger overlap in more lateral hippocampus, supporting ideas of parallel processing streams for these two categories. Our work helps to map the scene, in contrast to, face processing networks within, and connected to, the human hippocampus.

Temporal differences and commonalities between hand and tool neural processing

Object recognition is a complex cognitive process that relies on how the brain organizes object-related information. While spatial principles have been extensively studied, less studied temporal dynamics may also offer valuable insights into this process, particularly when neural processing overlaps for different categories, as it is the case of the categories of hands and tools. Here we focus on the differences and/or similarities between the time-courses of hand and tool processing under electroencephalography (EEG). Using multivariate pattern analysis, we compared, for different time points, classification accuracy for images of hands or tools when compared to images of animals. We show that for particular time intervals (~ 136–156 ms and ~ 252–328 ms), classification accuracy for hands and for tools differs. Furthermore, we show that classifiers trained to differentiate between tools and animals generalize their learning to classification of hand stimuli between ~ 260–320 ms and ~ 376–500 ms after stimulus onset. Classifiers trained to distinguish between hands and animals, on the other hand, were able to extend their learning to the classification of tools at ~ 150 ms. These findings suggest variations in semantic features and domain-specific differences between the two categories, with later-stage similarities potentially related to shared action processing for hands and tools.

Orientation representation in human visual cortices: contributions of non-visual information and action-related process.

Orientation processing in the human brain plays a crucial role in guiding grasping actions toward an object. Remarkably, despite the absence of visual input, the human visual cortex can still process orientation information. Instead of visual input, non-visual information, including tactile and proprioceptive sensory input from the hand and arm, as well as feedback from action-related processes, may contribute to orientation processing. However, the precise mechanisms by which the visual cortices process orientation information in the context of non-visual sensory input and action-related processes remain to be elucidated. Thus, our study examined the orientation representation within the visual cortices by analyzing the blood-oxygenation-level-dependent (BOLD) signals under four action conditions: direct grasp (DG), air grasp (AG), non-grasp (NG), and uninformed grasp (UG). The images of the cylindrical object were shown at +45° or - 45° orientations, corresponding to those of the real object to be grasped with the whole-hand gesture. Participants judged their orientation under all conditions. Grasping was performed without online visual feedback of the hand and object. The purpose of this design was to investigate the visual areas under conditions involving tactile feedback, proprioception, and action-related processes. To address this, a multivariate pattern analysis was used to examine the differences among the cortical patterns of the four action conditions in orientation representation by classification. Overall, significant decoding accuracy over chance level was discovered for the DG; however, during AG, only the early visual areas showed significant accuracy, suggesting that the object's tactile feedback influences the orientation process in higher visual areas. The NG showed no statistical significance in any area, indicating that without the grasping action, visual input does not contribute to cortical pattern representation. Interestingly, only the dorsal and ventral divisions of the third visual area (V3d and V3v) showed significant decoding accuracy during the UG despite the absence of visual instructions, suggesting that the orientation representation was derived from action-related processes in V3d and visual recognition of object visualization in V3v. The processing of orientation information during non-visually guided grasping of objects relies on other non-visual sources and is specifically divided by the purpose of action or recognition.

Decoding Eye Blink and Related EEG Activity in Realistic Working Environments.

Accurately evaluating cognitive load during work-related tasks in complex real-world environments is challenging, leading researchers to investigate the use of eye blinking as a fundamental pacing mechanism for segmenting EEG data and understanding the neural mechanisms associated with cognitive workload. Yet, little is known about the temporal dynamics of eye blinks and related visual processing in relation to the representation of task-specific information. Therefore, we analyzed EEG responses from two experiments involving simulated driving (re-active and pro-active) with three levels of task load for each, as well as operating a steam engine (active vs. passive), to decode the temporal dynamics of eye blink activity and the subsequent neural activity that follows blinking. As a result, we successfully decoded the binary representation of difficulty levels for pro-active driving using multivariate pattern analysis. However, the decoding level varied for different re-active driving conditions, which could be attributed to the required level of alertness. Furthermore, our study revealed that it was possible to decode both driving types as well as steam engine operating conditions, with the most significant decoding activity observed approximately 200 ms after a blink. Additionally, our findings suggest that eye blinks have considerable potential for decoding various cognitive states that may not be discernible through neural activity, particularly near the peak of the blink. The findings demonstrate the potential of blink-related measures alongside EEG data to decode cognitive states during complex tasks, with implications for improving evaluations of cognitive and behavioral states during tasks, such as driving and operating machinery.

Multivariate pattern analysis of EEG reveals nuanced impact of negation on sentence processing in the N400 and later time windows.

The neurocognitive mechanism underlying negation processing remains controversial. While negation is suggested to modulate the access of word meaning, no such evidence has been observed in the event-related potential (ERP) literature on sentence processing. In the current study, we applied both univariate ERP and multivariate pattern analysis (MVPA) methods to examine the processing of sentence negation. We investigated two types of negative congruent/incongruent sentence pairs with truth-value evaluation (e.g., "A robin is a/not a bird") and without (e.g., "The woman reads a/no book"). In the N400 time window, ERPs consistently showed increased negativity for negative and incongruent conditions. MVPA, on the other hand, revealed nuanced interactions between polarity and congruency. In the later P600 time window, MVPA but not the ERPs revealed an effect of congruency, which may be functionally distinct from the N400 window. We further used cross-decoding to show that the cognitive processes underlying the N400 window for both affirmative and negative sentences are comparable, whereas in the P600 window, only for the truth sentences, negative sentences showed a distinct pattern from their affirmative counterparts. Our results thus speak for a more interactive, but nevertheless serial and biphasic, and potentially construction-specific processing account of negation. We also discuss the advantage of applying MVPA in addition to the classical univariate methods for a better understanding of the neurobiology of negation processing and language comprehension alike.

Prolonged neural encoding of visual information in autism.

Autism spectrum disorder (ASD) is associated with a hyper-focused visual attentional style, impacting higher-order social and affective domains. The understanding of such peculiarity can benefit from the use of multivariate pattern analysis (MVPA) of high-resolution electroencephalography (EEG) data, which has proved to be a powerful technique to investigate the hidden neural dynamics orchestrating sensory and cognitive processes. Here, we recorded EEG in typically developing (TD) children and in children with ASD during a visuo-spatial attentional task where attention was exogenously captured by a small (zoom-in) or large (zoom-out) cue in the visual field before the appearance of a target at different eccentricities. MVPA was performed both in the cue-locked period, to reveal potential differences in the modulation of the attentional focus, and in the target-locked period, to reveal potential cascade effects on stimulus processing. Cue-locked MVPA revealed that while in the TD group the pattern of neural activity contained information about the cue mainly before the target appearance, the ASD group showed a temporally sustained and topographically diffuse significant decoding of the cue neural response even after the target onset, suggesting a delayed extinction of cue-related neural activity. Crucially, this delayed extinction positively correlated with behavioral measures of attentional hyperfocusing. Results of target-locked MVPA were coherent with a hyper-focused attentional profile, highlighting an earlier and stronger decoding of target neural responses in small cue trials in the ASD group. The present findings document a spatially and temporally overrepresented encoding of visual information in ASD, which can constitute one of the main reasons behind their peculiar cognitive style.

The Role of Agentive and Physical Forces in the Neural Representation of Motion Events.

How does the brain represent information about motion events in relation to agentive and physical forces? In this study, we investigated the neural activity patterns associated with observing animated actions of agents (e.g., an agent hitting a chair) in comparison to similar movements of inanimate objects that were either shaped solely by the physics of the scene (e.g., gravity causing an object to fall down a hill and hit a chair) or initiated by agents (e.g., a visible agent causing an object to hit a chair). Using an fMRI-based multivariate pattern analysis (MVPA), this design allowed testing where in the brain the neural activity patterns associated with motion events change as a function of, or are invariant to, agentive versus physical forces behind them. A total of 29 human participants (nine male) participated in the study. Cross-decoding revealed a shared neural representation of animate and inanimate motion events that is invariant to agentive or physical forces in regions spanning frontoparietal and posterior temporal cortices. In contrast, the right lateral occipitotemporal cortex showed a higher sensitivity to agentive events, while the left dorsal premotor cortex was more sensitive to information about inanimate object events that were solely shaped by the physics of the scene.

EEG-based multivariate pattern analysis reveals the control mechanisms of emotion regulation through distancing

Background/objectiveA neurocognitive model of distancing has systematically identified a set of brain regions that support the control mechanisms for emotion regulation (ER). However, the temporal dynamics of these control mechanisms during ER remains unclear. MethodTo address this issue, we recorded behavioral and electroencephalogram (EEG) data to compare proactive and reactive ER modes in an adapted ER task (N = 30 adults). In different ER modes, participants were instructed to downregulate their negative emotional experiences by applying the reappraisal tactic of distancing. ResultsThe behavioral results showed that proactive ER, which involves preparing for the upcoming regulation, reduced the negative emotional experience more than reactive ER, which involves no preparation process, in the reappraisal-negative condition. This indicated that proactive ER was more effective than reactive ER in regulating negative emotions. Event-related potential (ERP) and multivariate pattern analysis (MVPA) results showed that ER through distancing involved two phases: First, the reappraisal cue enhanced the allocation of attention to activate the mental building blocks and constructed a new perspective in the preparation process. Second, participants who benefited from the preparation process initiated the ER earlier and adaptively re-engaged in the ER if time permitted. ConclusionsTaken together, the control mechanisms underlying the preparation process influence the timing of ER, while the control mechanisms underlying the regulation process determine the regulatory effect.

Differential neurocognitive profiles in adult attention-deficit/hyperactivity disorder subtypes revealed by the Cambridge Neuropsychological Test Automated Battery.

Adult attention-deficit/hyperactivity disorder (aADHD) represents a heterogeneous entity incorporating different subgroups in terms of symptomatology, course, and neurocognition. Although neurocognitive dysfunction is generally associated with aADHD, its severity, association with self-reported symptoms, and differences between subtypes remain unclear. We investigated 61 outpatients (65.6% male, mean age 31.5 ± 9.5) diagnosed using DSM-5 criteria together with age-, sex-, and education-matched healthy controls (HC) (n = 58, 63.8% male, mean age 32.3 ± 9.6). Neurocognitive alterations were assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and compared between groups using the generalized linear model (GLM) method. Multivariate effects were tested by principal component analysis combined with multivariate pattern analysis. Self-reported symptom severity was tested for correlations with neurocognitive performance. GLM analyses revealed nominally significant differences between the aADHD and HC groups in several domains, however, only the Rapid Visual Information Processing measures survived correction, indicating impaired sustained attention and response inhibition in the aADHD group. Comparison of the predominantly inattentive and the hyperactive-impulsive/combined subtypes yielded nominally significant differences with higher levels of dysfunction in the inattentive group. In the stepwise discriminant analysis aADHD and HC groups were best separated with 2 factors representing sustained attention and reaction time. We found only weak correlations between symptom severity and CANTAB factors. aADHD patients are neuropsychologically heterogeneous and subtypes show different neurocognitive profiles. Differences between the aADHD and HC groups were driven primarily by the inattentive subtype. Sustained attention and its factor derivative showed the most significant alterations in aADHD patients.

Disrupted executive cerebro-cerebellar functional connectivity in alcohol use disorder.

Alcohol use disorder (AUD) affects 283 million people worldwide and its prevalence is increasing. Despite the role of the cerebellum in executive control and its sensitivity to alcohol, few studies have assessed its involvement in AUD-relevant functional networks. The goal of this study is to compare resting-state functional connectivity (FC) patterns in abstinent adults with a history of AUD and controls (CTL). We hypothesized that group differences in cerebro-cerebellar FC would be present, particularly within the frontoparietal/executive control network (FPN). Twenty-eight participants completed a resting-state functional magnetic resonance imaging (rsfMRI) study. CTL participants had no history of AUD, comorbid psychological conditions, or recent heavy drinking and/or drug use. AUD participants had a history of AUD, with sobriety for at least 30 days prior to data collection. Multivariate pattern analysis, an agnostic, whole-brain approach, was used to identify regions with significant differences in FC between groups. Seed-based analyses were then conducted to determine the directionality and extent of these FC differences. Associations between FC strength and executive function were assessed using correlations with Wisconsin Card Sorting Test (WCST) performance. There were significant group differences in FC in nodes of the FPN, ventral attention network, and default mode network. Post hoc analyses predominantly identified FC differences within the cerebro-cerebellar FPN, with AUD showing significantly less FC within the FPN. In AUD, FC strength between FPN clusters identified in the multivariate pattern analysis (MVPA) analysis (Left Crus II, Right Frontal Cortex) was positively associated with performance on the WCST. Our results show less engagement of the FPN in individuals with AUD than in CTL. FC strength within this network was positively associated with performance on the WCST. These findings suggest that long-term heavy drinking alters cerebro-cerebellar FC, particularly within networks that are involved in executive function.

Decoding the content of working memory in school-aged children

Developmental improvements in working memory (WM) maintenance predict many real-world outcomes, including educational attainment. It is thus critical to understand which WM mechanisms support these behavioral improvements, and how WM maintenance strategies might change through development. One challenge is that specific WM neural mechanisms cannot easily be measured behaviorally, especially in a child population. However, new multivariate decoding techniques have been designed, primarily in adult populations, that can sensitively decode the contents of WM. The goal of this study was to deploy multivariate decoding techniques known to decode memory representations in adults to decode the contents of WM in children. We created a simple computerized WM game for children, in which children maintained different categories of information (visual, spatial or verbal). We collected electroencephalography (EEG) data from 20 children (7–12-year-olds) while they played the game. Using Multivariate Pattern Analysis (MVPA) on children's EEG signals, we reliably decoded the category of the maintained information during the sensory and maintenance period. Across exploratory reliability and validity analyses, we examined the robustness of these results when trained on less data, and how these patterns generalized within individuals throughout the testing session. Furthermore, these results matched theory-based predictions of WM across individuals and across ages. Our proof-of-concept study proposes a direct and age-appropriate potential alternative to exclusively behavioral WM maintenance measures in children. Our study demonstrates the utility of MVPA to measure and track the uninstructed representational content of children's WM. Future research could use our technique to investigate children's WM maintenance and strategies.

Neural processing of bottom-up perception of biological motion under attentional load

Considering its importance for one’s survival and social significance, biological motion (BM) perception is assumed to occur automatically. Previous behavioral results showed that task-irrelevant BM in the periphery interfered with task performance at the fovea. Under selective attention, BM perception is supported by a network of regions including the occipito-temporal (OTC), parietal, and premotor cortices. Retinotopy studies that use BM stimulus showed distinct maps for its processing under and away from selective attention. Based on these findings, we investigated how bottom-up perception of BM would be processed in the human brain under attentional load when it was shown away from the focus of attention as a task-irrelevant stimulus. Participants (N = 31) underwent an fMRI study in which they performed an attentionally demanding visual detection task at the fovea while intact or scrambled point light displays of BM were shown at the periphery. Our results showed the main effect of attentional load in fronto-parietal regions and both univariate activity maps and multivariate pattern analysis results support the attentional load modulation on the task-irrelevant peripheral stimuli. However, this effect was not specific to intact BM stimuli and was generalized to motion stimuli as evidenced by the motion-sensitive OTC involvement during the presence of dynamic stimuli in the periphery. These results confirm and extend previous work by showing that task-irrelevant distractors can be processed by stimulus-specific regions when there are enough attentional resources available. We discussed the implications of these results for future studies.

Compressibility Analysis of Functional Near-Infrared Spectroscopy Signals in Children With Attention-Deficit/Hyperactivity Disorder.

Functional near- infrared spectroscopy (fNIRS) as an emerging optical neuroimaging technique has attracted the interest and attention of many investigators. With the growth of fNIRS data volume, effective data compression methods are urgent. Compressive sensing (CS) has been demonstrated a promising tool to deal with biomedical data. However, whether the compressibility of fNIRS data can discriminate different brain states is unclear. In this study, the fNIRS signals from fifteen attention-deficit/hyperactivity disorder (ADHD) children and fifteen typically developing (TD) children were recorded during an N-back task and a Go/NoGo task respectively. A block sparse Bayesian learning-based CS method was used to reconstruct the compressed fNIRS data. To assess the performance of the CS method, we adopted two metrics, structural similarity index (SSIM) and mean squared error (MSE), both of them effective in evaluating the compressibility of fNIRS data. Then, the two metrics were analyzed to discriminate the brain states of ADHD children and TD children during the two tasks using the multivariate pattern analysis (MVPA) method. As indicated by the results, the CS method could reconstruct the compressed fNIRS data with a high reconstruction quality at different compression ratio ([Formula: see text] and [Formula: see text]). Furthermore, the MVPA method could distinguish different brain states with high accuracy, and identify that the prefrontal cortex is a key brain region for distinguishing ADHD vs. TD or N-back vs. Go/NoGo. These findings indicated that CS is very promising for the storage and transmission of massive fNIRS data, and the compressibility of fNIRS data is a potential biomarker for the diagnosis of ADHD.

CE Workshop 10: Advances in Event-Related Potential Methods for Assessing Clinical Populations

Abstract & Learning Objectives:Event-related potentials (ERPs) have been used to examine perceptual, cognitive, motor, and affective processes for over 50 years. Although newer techniques provide greater neuroanatomical specificity, the excellent temporal resolution and ease of acquisition of ERPs continue to be highly valuable. In addition, continued methodological refinements have made it possible to answer progressively more sophisticated questions using ERPs. In this presentation, I will describe several key methodological improvements that are now in widespread use or are on the verge of becoming widely used. This will include improved recording methods, such as innovations in electrodes that allow both fast and low-noise EEG acquisition. It will also include EEG preprocessing methods that minimize artifacts and increase the signal-to-noise ratio of the ERPs. Finally, it will include multivariate pattern analysis methods that can be used to "decode" what a participant perceives and stores in working memory. Together, these new methods have dramatically increased the information that can be obtained about dysfunctional brain activity in clinical populations.Upon conclusion of this course, learners will be able to: 1.Evaluate and critically assess ERP studies of clinical populations that were published over the last decade, making use of advanced recording and analysis methods that are now widely used2.Evaluate and critically assess ERP studies of clinical populations that have been published recently or will be published in the near future that take advantage of state-of-the-art recording and analysis methods, such as dry electrodes and multivariate pattern analysis3.Integrate recent and emerging research findings into your research or clinical practice

The roles of edge-based and surface-based information in the dynamic neural representation of objects

We combined multivariate pattern analysis (MVPA) and electroencephalogram (EEG) to investigate the role of edge, color, and other surface information in the neural representation of visual objects. Participants completed a one-back task in which they were presented with color photographs, grayscale images, and line drawings of animals, tools, and fruits. Our results provide the first neural evidence that line drawings elicit similar neural activities as color photographs and grayscale images during the 175–305 ms window after the stimulus onset. Furthermore, we found that other surface information, rather than color information, facilitates decoding accuracy in the early stages of object representations and affects the speed of this. These results provide new insights into the role of edge-based and surface-based information in the dynamic process of neural representations of visual objects.

Your place or mine? The neural dynamics of personally familiar scene recognition suggests category independent familiarity encoding.

Recognizing a stimulus as familiar is an important capacity in our everyday life. Recent investigation of visual processes has led to important insights into the nature of the neural representations of familiarity for human faces. Still, little is known about how familiarity affects the neural dynamics of non-face stimulus processing. Here we report the results of an EEG study, examining the representational dynamics of personally familiar scenes. Participants viewed highly variable images of their own apartments and unfamiliar ones, as well as personally familiar and unfamiliar faces. Multivariate pattern analyses were used to examine the time course of differential processing of familiar and unfamiliar stimuli. Time-resolved classification revealed that familiarity is decodable from the EEG data similarly for scenes and faces. The temporal dynamics showed delayed onsets and peaks for scenes as compared to faces. Familiarity information, starting at 200ms, generalized across stimulus categories and led to a robust familiarity effect. In addition, familiarity enhanced category representations in early (250-300ms) and later (>400ms) processing stages. Our results extend previous face familiarity results to another stimulus category and suggest that familiarity as a construct can be understood as a general, stimulus-independent processing step during recognition.

Altered effective connectivity from cerebellum to motor cortex in chronic low back pain: A multivariate pattern analysis and spectral dynamic causal modeling study

To explore the central processing mechanism of pain perception in chronic low back pain (cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional amplitude of low-frequency fluctuations (fALFF) analysis, and spectral dynamic causal modeling (spDCM). Thirty-two patients with cLBP and 29 matched healthy controls (HCs) for the first cohort and 24 patients with cLBP and 22 HCs for the validation cohort underwent resting-state fMRI scan. The alterations in static and dynamic fALFF were as classification features to distinguish patients with cLBP from HCs. The brain regions gotten from the MVPA results were used for further spDCM analysis. We found that the most discriminative brain regions that contributed to the classification were the right supplementary motor area (SMA.R), left paracentral lobule (PCL.L), and bilateral cerebellar Crus II. The spDCM results displayed decreased excitatory influence from the bilateral cerebellar Crus II to PCL.L in patients with cLBP compared with HCs. Moreover, the conversion of effective connectivity from the bilateral cerebellar Crus II to SMA.R from excitatory influence to inhibitive influence, and the effective connectivity strength exhibited partially mediated effects on Chinese Short Form Oswestry Disability Index Questionnaire (C-SFODI) scores. Our findings suggest that the cerebellum and its weakened or inhibited connections to the motor cortex may be one of the underlying feedback pathways for pain perception in cLBP, and partially mediate the degree of dysfunction.

Role of articulatory motor networks in perceptual categorization of speech signals: a 7T fMRI study.

Speech and language processing involve complex interactions between cortical areas necessary for articulatory movements and auditory perception and a range of areas through which these are connected and interact. Despite their fundamental importance, the precise mechanisms underlying these processes are not fully elucidated. We measured BOLD signals from normal hearing participants using high-field 7 Tesla fMRI with 1-mm isotropic voxel resolution. The subjects performed 2 speech perception tasks (discrimination and classification) and a speech production task during the scan. By employing univariate and multivariate pattern analyses, we identified the neural signatures associated with speech production and perception. The left precentral, premotor, and inferior frontal cortex regions showed significant activations that correlated with phoneme category variability during perceptual discrimination tasks. In addition, the perceived sound categories could be decoded from signals in a region of interest defined based on activation related to production task. The results support the hypothesis that articulatory motor networks in the left hemisphere, typically associated with speech production, may also play a critical role in the perceptual categorization of syllables. The study provides valuable insights into the intricate neural mechanisms that underlie speech processing.