fMRI Responses Research Articles

Functional and diagnostic significance of the fMRI-response type to motor loads in patients after traumatic brain damage

Aim of the study. Clarification of the functional and diagnostic significance of topography, or the type of fMRI-response recorded during active and passive hand movements in patients after traumatic brain injury.Material and methods. fMRI-responses gained during active and passive hand movements were analyzed in 40 patients with posttraumatic motor function disturbances and compared with results gained from 17 healthy volunteers (control group ).Results. In analyzed patients the increase of percentage of diffuse fMRI-response has been shown along with the areas of activation not typical for movement activation pattern typical healthy volunteers. The fMRI response type being it local or multifocal does not clearly correlate with the presence of motor function impairment (hemiparesis). However, it was found that with greater severity of hemiparesis there is a larger percentage of multifocal fMRI-response.The transition from a multifocal form of a motor fMRI-response to a local one observed in a dynamic study is accompanied by an improvement of patient’s general condition, a shift towards normalization of a number of morphofunctional indicators of the central nervous system, the tendency of regression of motor disorders.Conclusion. The increase of multifocal fMRI-responses in patients after traumatic brain injury is one of the signs of cerebral dysfunction. Dynamically observed transformation from multifocal to local fMRI-responses is associated with current or long-term improvement in motor activity as well, wit the regression of other clinical impairments and can be considered as prognostically positive sign of the course of post-traumatic illness.

Dissecting Motor and Cognitive Component Processes of a Finger-Tapping Task With Hybrid Dopamine Positron Emission Tomography and Functional Magnetic Resonance Imaging.

Striatal dopamine is involved in facilitation of motor action as well as various cognitive and emotional functions. Positron emission tomography (PET) is the primary imaging method used to investigate dopamine function in humans. Previous PET studies have shown striatal dopamine release during simple finger tapping in both the putamen and the caudate. It is likely that dopamine release in the putamen is related to motor processes while dopamine release in the caudate could signal sustained cognitive component processes of the task, but the poor temporal resolution of PET has hindered firm conclusions. In this study we simultaneously collected [11C]Raclopride PET and functional Magnetic Resonance Imaging (fMRI) data while participants performed finger tapping, with fMRI being able to isolate activations related to individual tapping events. The results revealed fMRI-PET overlap in the bilateral putamen, which is consistent with a motor component process. Selective PET responses in the caudate, ventral striatum, and right posterior putamen, were also observed but did not overlap with fMRI responses to tapping events, suggesting that these reflect non-motor component processes of finger tapping. Our findings suggest an interplay between motor and non-motor-related dopamine release during simple finger tapping and illustrate the potential of hybrid PET-fMRI in revealing distinct component processes of cognitive functions.

Functional magnetic resonance imaging responses during perceptual decision-making at 3 and 7T in human cortex, striatum, and brainstem.

While functional magnetic resonance imaging (fMRI) at ultra‐high field (7 T) promises a general increase in sensitivity compared to lower field strengths, the benefits may be most pronounced for specific applications. The current study aimed to evaluate the relative benefit of 7 over 3 T fMRI for the assessment of responses evoked in different brain regions by a well‐controlled cognitive task. At 3 and 7 T, the same participants made challenging perceptual decisions about visual motion combined with monetary rewards for correct choices. Previous work on this task has extensively characterized the underlying cognitive computations and single‐cell responses in cortical and subcortical structures. We quantified the evoked fMRI responses in extrastriate visual cortical areas, the striatum, and the brainstem during the decision interval and the post‐feedback interval of the task. The dependence of response amplitudes on field strength during the decision interval differed between cortical, striatal, and brainstem regions, with a generally bigger 7 versus 3 T benefit in subcortical structures. We also found stronger responses during relatively easier than harder decisions at 7 T for dopaminergic midbrain nuclei, in line with reward expectation. Our results demonstrate the potential of 7 T fMRI for illuminating the contribution of small brainstem nuclei to the orchestration of cognitive computations in the human brain.

Direct comparison of contralateral bias and face/scene selectivity in human occipitotemporal cortex

Human visual cortex is organised broadly according to two major principles: retinotopy (the spatial mapping of the retina in cortex) and category-selectivity (preferential responses to specific categories of stimuli). Historically, these principles were considered anatomically separate, with retinotopy restricted to the occipital cortex and category-selectivity emerging in the lateral-occipital and ventral-temporal cortex. However, recent studies show that category-selective regions exhibit systematic retinotopic biases, for example exhibiting stronger activation for stimuli presented in the contra- compared to the ipsilateral visual field. It is unclear, however, whether responses within category-selective regions are more strongly driven by retinotopic location or by category preference, and if there are systematic differences between category-selective regions in the relative strengths of these preferences. Here, we directly compare contralateral and category preferences by measuring fMRI responses to scene and face stimuli presented in the left or right visual field and computing two bias indices: a contralateral bias (response to the contralateral minus ipsilateral visual field) and a face/scene bias (preferred response to scenes compared to faces, or vice versa). We compare these biases within and between scene- and face-selective regions and across the lateral and ventral surfaces of the visual cortex more broadly. We find an interaction between surface and bias: lateral surface regions show a stronger contralateral than face/scene bias, whilst ventral surface regions show the opposite. These effects are robust across and within subjects, and appear to reflect large-scale, smoothly varying gradients. Together, these findings support distinct functional roles for the lateral and ventral visual cortex in terms of the relative importance of the spatial location of stimuli during visual information processing.

Relationships between cerebrovascular reactivity, visual-evoked functional activity, and resting-state functional connectivity in the visual cortex and basal forebrain in glaucoma.

Glaucoma is primarily considered an eye disease with widespread involvements of the brain. Yet, it remains unclear how cerebrovasculature is regulated in glaucoma and how different brain regions coordinate functionally across disease severity. To address these questions, we applied a novel whole-brain relative cerebrovascular reactivity (rCVR) mapping technique using resting-state functional magnetic resonance imaging (fMRI) without gas challenges to 38 glaucoma patients and 21 healthy subjects. The relationships between rCVR, visual-evoked fMRI response, and resting-state functional connectivity in glaucoma were then established. In the visual cortex, rCVR has a decreasing trend with glaucoma severity (p<0.05), and is coupled with visual-evoked response and functional connectivity in both hemispheres (p<0.001). Interestingly, rCVR in the basal forebrain (BF) has an increasing trend with glaucoma severity (p<0.05). The functional connectivity between right diagonal band of Broca (a sub-region of BF) and lateral visual cortex decreases with glaucoma (p<0.05), while such connectivity is inversely coupled with rCVR in the BF (p<0.05), but not the visual cortex. Overall, we demonstrate opposite trends of rCVR changes in the visual cortex and BF in glaucoma patients, suggestive of compensatory actions in vascular reserve between the two brain regions. The neurovascular coupling within the visual cortex appears deteriorated in glaucoma, whereas the association between BF-visual cortex functional connectivity and rCVR of BF indicates the functional and vascular involvements in glaucoma beyond the primary visual pathway.

Neural basis of distractor resistance during visual working memory maintenance

Visual working memory representations must be protected from the intervening irrelevant visual input. While it is well known that interference resistance is most challenging when distractors match the prioritised mnemonic information, its neural mechanisms remain poorly understood. Here, we identify two top-down attentional control processes that have opposing effects on distractor resistance. We reveal an early selection negativity in the EEG responses to matching as compared to non-matching distractors, the magnitude of which is negatively associated with behavioural distractor resistance. Additionally, matching distractors lead to reduced post-stimulus alpha power as well as increased fMRI responses in the object-selective visual cortical areas and the inferior frontal gyrus. However, the congruency effect found on the post-stimulus periodic alpha power and the inferior frontal gyrus fMRI responses show a positive association with distractor resistance. These findings suggest that distractor interference is enhanced by proactive memory content-guided selection processes and diminished by reactive allocation of top-down attentional resources to protect memorandum representations within visual cortical areas retaining the most selective mnemonic code.

Effect of mGluR2 positive allosteric modulation on frontostriatal working memory activation in schizophrenia.

Negative symptoms and cognitive deficits contribute strongly to disability in schizophrenia, and are resistant to existing medications. Recent drug development has targeted enhanced NMDA function by increasing mGluR2/3 signaling. However, the clinical utility of such agents remains uncertain, and markers of brain circuit function are critical for clarifying mechanisms and understanding individual differences in efficacy. We conducted a double-blind, placebo-controlled, randomized cross-over (14 day washout) pilot study evaluating adjunctive use of the mGluR2 positive allosteric modulator AZD8529 (80mg daily for 3 days), in chronic stable patients with schizophrenia (n=26 analyzed). We focused on 3T fMRI response in frontostriatal regions during an n-back working memory task, testing the hypothesis that AZD8529 produces fMRI changes that correlate with improvement in negative symptoms and cognition. We found that AZD8529 did not produce significant group-average effects on symptoms or cognitive accuracy. However, AZD8529 did increase n-back fMRI activation in striatum (p<0.0001) and anterior cingulate/paracingulate (p=0.002). Greater drug-versus-placebo effects on caudate activation significantly correlated with greater reductions in PANSS negative symptom scores (r=−0.42, p=0.031), and exploratory correlations suggested broader effects across multiple symptom domains and regions of interest. These findings demonstrate that fMRI responses to mGluR2 positive modulation relate to individual differences in symptom reduction, and could be pursued for future biomarker development. Negative clinical results at the group level should not lead to premature termination of investigation of this mechanism which may benefit an important subset of individuals with schizophrenia. Imaging biomarkers may reveal therapeutic mechanisms, and help guide treatment towards specific populations.

Spatially Adjacent Regions in Posterior Cingulate Cortex Represent Familiar Faces at Different Levels of Complexity.

Extensive research has shown that perceptual information of faces is processed in a network of hierarchically-organized areas within ventral temporal cortex. For familiar and famous faces, perceptual processing of faces is normally accompanied by extraction of semantic knowledge about the social status of persons. Semantic processing of familiar faces could entail progressive stages of information abstraction. However, the cortical mechanisms supporting multistage processing of familiar faces have not been characterized. Here, using an event-related fMRI experiment, familiar faces from four celebrity groups (actors, singers, politicians, and football players) and unfamiliar faces were presented to the human subjects (both males and females) while they were engaged in a face categorization task. We systematically explored the cortical representations for faces, familiar faces, subcategories of familiar faces, and familiar face identities using whole-brain univariate analysis, searchlight-based multivariate pattern analysis (MVPA), and functional connectivity analysis. Convergent evidence from all these analyses revealed a set of overlapping regions within posterior cingulate cortex (PCC) that contained decodable fMRI responses for representing different levels of semantic knowledge about familiar faces. Our results suggest a multistage pathway in PCC for processing semantic information of faces, analogous to the multistage pathway in ventral temporal cortex for processing perceptual information of faces.SIGNIFICANCE STATEMENT Recognizing familiar faces is an important component of social communications. Previous research has shown that a distributed network of brain areas is involved in processing the semantic information of familiar faces. However, it is not clear how different levels of semantic information are represented in the brain. Here, we evaluated the multivariate response patterns across the entire cortex to discover the areas that contain information for familiar faces, subcategories of familiar faces, and identities of familiar faces. The searchlight maps revealed that different levels of semantic information are represented in topographically adjacent areas within posterior cingulate cortex (PCC). The results suggest that semantic processing of faces is mediated through progressive stages of information abstraction in PCC.

Reemergence of the language network during recovery from severe traumatic brain injury: A pilot functional MRI study

ABSTRACT Primary Objective We hypothesized that, in patients with acute severe traumatic brain injury (TBI) who recover basic language function, speech-evoked blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) responses within the canonical language network increase over the first 6 months post-injury. Research Design We conducted a prospective, longitudinal fMRI pilot study of adults with acute severe TBI admitted to the intensive care unit. We also enrolled age- and sex-matched healthy subjects. Methods and Procedures We evaluated BOLD signal in bilateral superior temporal gyrus (STG) and inferior frontal gyrus (IFG) regions of interest acutely and approximately 6 months post-injury. Given evidence that regions outside the canonical language network contribute to language processing, we also performed exploratory whole-brain analyses. Main Outcomes and Results Of the 16 patients enrolled, eight returned for follow-up fMRI, all of whom recovered basic language function. We observed speech-evoked longitudinal BOLD increases in the left STG, but not in the right STG, right IFG, or left IFG. Whole-brain analysis revealed increases in the right supramarginal and middle temporal gyri but no differences between patients and healthy subjects (n = 16). Conclusion This pilot study suggests that, in patients with severe TBI who recover language function, speech-evoked responses in bihemispheric language-processing cortex reemerge by 6 months post-injury.

Gender discrimination facilitates fMRI responses and connectivity to thermal pain

Gender discrimination is a serious social issue that has been shown to increase negative consequences, especially in females when accompanied by acute or chronic pain. Experiencing social pain through discrimination can increase an individual's evaluation of evoked physical pain. However, few studies have explored the mechanism underlying how gender discrimination modulates brain responses when individuals experience physical pain evoked by noxious stimuli. In this study, we addressed this issue using a gender discrimination fMRI paradigm with thermal pain stimulation. We found that discrimination indeed affected participants’ own behavioral self-evaluation of noxious stimuli. Discrimination-encoded brain activations were identified in the temporopolar cortex, while brain activations to thermal stimuli after viewing pictures of discrimination were found in the dorsal anterior cingulate cortex (dACC). Brain activations in the temporopolar cortex and the dACC were correlated. Furthermore, pain perception-specific functional connectivity of the dACC-SII in the cue stage and the dACC-frontal in the pain stage were identified, suggesting a facilitative effect of gender discrimination on females’ experience of physical pain. Our results indicate that the dACC may play a central role in mediating the affective aspect of physical pain after experiencing discrimination. These findings provide novel insights into the underlying mechanism of how gender discrimination facilitates females’ experience of physical pain.

Symmetry Processing in the Macaque Visual Cortex

Symmetry is a highly salient feature of the natural world that is perceived by many species. In humans, the cerebral areas processing symmetry are now well identified from neuroimaging measurements. Macaque could constitute a good animal model to explore the underlying neural mechanisms, but a previous comparative study concluded that functional magnetic resonance imaging responses to mirror symmetry in this species were weaker than those observed in humans. Here, we re-examined symmetry processing in macaques from a broader perspective, using both rotation and reflection symmetry embedded in regular textures. Highly consistent responses to symmetry were found in a large network of areas (notably in areas V3 and V4), in line with what was reported in humans under identical experimental conditions. Our results suggest that the cortical networks that process symmetry in humans and macaques are potentially more similar than previously reported and point toward macaque as a relevant model for understanding symmetry processing.

Double-wavelet transform for multi-subject resting state functional magnetic resonance imaging data.

Conventional regions of interest (ROIs)-level resting state fMRI (functional magnetic resonance imaging) response analyses do not rigorously model the underlying spatial correlation within each ROI. This can result in misleading inference. Moreover, they tend to estimate the temporal covariance matrix with the assumption of stationary time series, which may not always be valid. To overcome these limitations, we propose a double-wavelet approach that simplifies temporal and spatial covariance structure because wavelet coefficients are approximately uncorrelated under mild regularity conditions. This property allows us to analyze much larger dimensions of spatial and temporal resting-state fMRI data with reasonable computational burden. Another advantage of our double-wavelet approach is that it does not require the stationarity assumption. Simulation studies show that our method reduced false positive and false negative rates by properly taking into account spatial and temporal correlations in data. We also demonstrate advantages of our method by using resting-state fMRI data to study the difference in resting-state functional connectivity between healthy subjects and patients with major depressive disorder.

Can MEG Source Localization Reveal the Time Course of Processing in the FFA, PPA, and EBA?

fMRI has provided a rich spatial map of category-selective responses across the ventral visual cortex, and EEG/MEG can inform the precise time course of visual processing. But what we really need is both temporal and spatial precision simultaneously. Can MEG source localization reveal the time course of processing in specific functional regions, distinguishing them from nearby regions? Here we addressed that question by collecting both MEG and fMRI responses from the same fifteen participants to the same images of faces, bodies, scenes, and objects. We used fMRI localizers to define the FFA, PPA, and EBA in each participant individually. We then computed source estimates from the MEG data using dynamic statistical parametric mapping, extracting the time courses of estimated MEG source amplitudes to each stimulus category within each fMRI-derived ROI. Source-localized MEG data showed a selective response to bodies peaking at about 160 ms in bilateral EBA that was not present in the FFA or PPA. A highly prominent right-lateralized face response peaking around 130 ms was detected in all three fROIs, while no clear peak response to scenes was present in any fROI. Analyses of source-localized responses across the whole cortex revealed a similar picture, with scene-selective responses spatially blurred across the occipital cortex, right-lateralized face-selective responses spanning much of the ventral surface of the cortex (extending well beyond the fusiform gyrus), and body-selective responses arising later on the lateral surface of both hemispheres in the vicinity of the EBA. These results suggest that right-lateralized face processing peaks on the ventral surface of the brain around 130 ms and body-selective processing on the lateral surface around 160 ms. However, they also indicate that MEG source localization is unable to detect fMRI-expected signals from some regions (PPA) or to precisely localize signals to the spatial grain of fMRI-defined functional regions.

Deep neural network models of visual cortex reveal curvature and real-world size as organizing principles of mid-level representation

Mid-level visual features directly support an array of behaviors; thus, they may be critical for understanding the functional organization of visual cortex. However, attempts at characterizing mid-level features have been hampered by the difficulty of describing these features in words—they exist in an “ineffable valley” between the describable patterns of low-level vision (e.g., edges) and the commonsense concepts of visual cognition (e.g., objects). Here we developed a novel approach to identify interpretable emergent properties of mid-level representations in deep neural network (DNN) models of visual cortex. Using this approach, we examined DNN models that were fit to scene-evoked fMRI responses in category-selective regions of visual cortex—specifically, scene-selective cortex (sceneDNN) and object-selective cortex (objectDNN). Our method uses a semantically-guided image-occlusion procedure to systematically characterize how DNN activations are driven by the classes of objects within a scene. We examined the relationship between mid-level features and several object properties that have previously been associated with response preferences in visual cortex: curvature, real-world size, animacy, naturalness, and spatial stability. We found that while mid-level features appear complex and difficult to describe at a surface level, large-scale computational analyses can reveal a latent underlying relationship to interpretable object properties. Specifically, we found that the mid-level representations of the sceneDNN support a latent preference for objects that are boxy and large in real-world size. In contrast, mid-level representations of the objectDNN support a complementary preference for objects that are curvy and small in real-world size. These effects were robust to variations of model hyperparameters and were reproducible across different DNN models. Our findings show that curvature and real-world size are emergent organizing principles of mid-level visual representation, and they suggest that differences in mid-level feature tuning may be critical for understanding the organization of visual cortex into category-selective patches.

A Signature of Orientation Invariance in Human fMRI &amp; (Some) Deep Neural Networks

Deep neural network (DNN) models capture the representational geometry of neural responses to different objects (Yamins & Dicarlo, 2016), but do they show the same hallmark invariances as human vision? In the human visual system, orientation invariant representations (consistent responses to an item viewed at different angles) emerge abruptly between V3 and LOC (Morgan & Alvarez, 2014). Here, we ask where (if anywhere) orientation invariance emerges in DNNs, and whether that invariance mirrors the invariance we observe in humans. Using a large battery of DNNs (both trained and randomly initialized, from model zoos including Torchvision and the Taskonomy project), we extract the responses of each layer of each network to a series of 8 stimuli at 5 different degrees of rotation (0-45-90-135-180) – the same stimulus set used in the human fMRI. We then assess the representational similarity of each stimulus to the same stimulus at different rotations (“within-category” similarity) and to different stimuli at the same rotation (“across-category” similarity) in each layer of the network and each area of the brain. We compare the DNN responses directly to the fMRI responses with a second Pearson distance metric across the aggregated similarities (within- and across-category). In the end, this process produces a similarity score for each DNN layer to each area of the brain. Smoothing across the layer-wise similarity scores in each network with a generalized additive model (GAM), we show that in the majority of trained models we test, earlier and intermediate layers of the network are more similar to brain areas with little to no invariance (EVC / OPC) and later layers (usually following a fully connected layer) are more similar to brain areas with strong invariance (LOC / OTC). Overall, our results provide a preliminary signature of human brain-like orientation invariance in deep neural networks.

High-performing computational models of visual cortex are marked by high intrinsic dimensionality

A central goal of cognitive neuroscience is to understand the computational properties of neural representations in visual cortex. Above and beyond the information content or visual features encoded in neural populations, we wish to understand the encoding format itself and the computations it subserves. These questions are at the heart of research into computational properties such as sparsity, dimensionality, manifold geometry, invariance, and dynamics in both biological and artificial neural networks. Here we investigate the computational properties of representations across human visual cortex using fMRI encoding models. We first fit voxelwise encoding models to predict fMRI responses to images of natural objects and scenes using both hand-engineered feature detectors (e.g. edge detectors) and pre-trained and untrained convolutional neural networks (CNNs). We then performed statistical analyses of the encoding model regressors to explore how performance is affected by several computational properties, including mixed selectivity and intrinsic dimensionality. We searched for the properties that correlated best with encoding accuracy. We find evidence for high intrinsic dimensionality as an important driver of encoding model performance across the ventral and dorsal visual streams. Furthermore, we find that several mechanisms for increasing intrinsic dimensionality lead to similar improvements in encoding model performance, including division normalization, multiplicative interaction, random nonlinear projection, and supervised learning for image classification. Thus, it appears that intrinsic dimensionality itself best explains the improvements of these varied representational mechanisms. Together, our work explores a methodology for investigating the computational properties of neural representations through an ensemble of encoding models and statistical analyses of their computational properties. We use this approach to identify high intrinsic dimensionality as a key predictor of encoding model performance, suggesting that biological vision relies on representations that are highly efficient, and may allow for the linear separability of a large number of visual properties across different tasks.

GLMsingle: a turnkey solution for accurate single-trial fMRI response estimates

GLMsingle: a turnkey solution for accurate single-trial fMRI response estimates

Cross-dataset reproducibility of human retinotopic maps

Population receptive field (pRF) models fit to fMRI data are used to non-invasively measure retinotopic maps in human visual cortex, and these maps are a fundamental component of visual neuroscience experiments. Here, we examined the reproducibility of retinotopic maps across two datasets: a newly acquired retinotopy dataset from New York University (NYU) (n = 44) and a public dataset from the Human Connectome Project (HCP) (n = 181). Our goal was to assess the degree to which pRF properties are similar across datasets, despite substantial differences in their experimental protocols. The two datasets simultaneously differ in their stimulus apertures, participant pool, fMRI protocol, MRI field strength, and preprocessing pipeline. We assessed the cross-dataset reproducibility of the two datasets in terms of the similarity of vertex-wise pRF estimates and in terms of large-scale polar angle asymmetries in cortical magnification. Within V1, V2, V3, and hV4, the group-median NYU and HCP vertex-wise polar angle estimates were nearly identical. Both eccentricity and pRF size estimates were also strongly correlated between the two datasets, but with a slope different from 1; the eccentricity and pRF size estimates were systematically greater in the NYU data. Next, to compare large-scale map properties, we quantified two polar angle asymmetries in V1 cortical magnification previously identified in the HCP data. The NYU dataset confirms earlier reports that more cortical surface area represents horizontal than vertical visual field meridian, and lower than upper vertical visual field meridian. Together, our findings show that the retinotopic properties of V1, V2, V3, and hV4 can be reliably measured across two datasets, despite numerous differences in their experimental design. fMRI-derived retinotopic maps are reproducible because they rely on an explicit computational model of the fMRI response. In the case of pRF mapping, the model is grounded in physiological evidence of how visual receptive fields are organized, allowing one to quantitatively characterize the BOLD signal in terms of stimulus properties (i.e., location and size). The new NYU Retinotopy Dataset will serve as a useful benchmark for testing hypotheses about the organization of visual areas and for comparison to the HCP 7T Retinotopy Dataset.

Gait-Related Brain Activation During Motor Imagery of Complex and Simple Ambulation in Parkinson's Disease With Freezing of Gait.

Background: Freezing of gait (FOG) in Parkinson's disease (PD) is a devastating clinical phenomenon that has a detrimental impact on patients. It tends to be triggered more often during turning (complex) than during forwarding straight (simple) walking. The neural mechanism underlying this phenomenon remains unclear and requires further elucidation.Objective: To investigate the differences in cerebral functional magnetic resonance imaging responses between PD patients with and without FOG during explicitly video-guided motor imagery (MI) of various complex (normal, freezing) and simple (normal, freezing) walking conditions.Methods: We recruited 34 PD patients, namely, 20 with FOG and 14 without FOG, and 15 normal controls. Participants underwent video-guided MI of turning and straight walking, with and without freezing, while their brain blood oxygen level-dependent (BOLD) activities were measured. Gait analysis was performed.Results: While comparing FOG turning with FOG straight walking, freezers showed higher activation of the superior occipital gyrus, left precentral gyrus, and right postcentral gyrus compared with non-freezers. Normal controls also manifest similar findings compared with non-freezers, except no difference was noted in occipital gyrus activity between the two groups. Freezers also displayed a higher effect size in the locomotor regions than non-freezers during imagery of normal turning.Conclusions: Our findings suggest that freezers require a higher drive of cortical and locomotion regions to overcome the overinhibition of the pathways in freezers than in non-freezers. Compared with simple walking, increased dorsal visual pathway and deep locomotion region activities might play pivotal roles in tackling FOG in freezers during complex walking.

Controlling for Spurious Nonlinear Dependence in Connectivity Analyses.

Recent analysis methods can capture nonlinear interactions between brain regions. However, noise sources might induce spurious nonlinear relationships between the responses in different regions. Previous research has demonstrated that traditional denoising techniques effectively remove noise-induced linear relationships between brain areas, but it is unknown whether these techniques can remove spurious nonlinear relationships. To address this question, we analyzed fMRI responses while participants watched the film Forrest Gump. We tested whether nonlinear Multivariate Pattern Dependence Networks (MVPN) outperform linear MVPN in non-denoised data, and whether this difference is reduced after CompCor denoising. Whereas nonlinear MVPN outperformed linear MVPN in the non-denoised data, denoising removed these nonlinear interactions. We replicated our results using different neural network architectures as the bases of MVPN, different activation functions (ReLU and sigmoid), different dimensionality reduction techniques for CompCor (PCA and ICA), and multiple datasets, demonstrating that CompCor's ability to remove nonlinear interactions is robust across these analysis choices and across different groups of participants. Finally, we asked whether information contributing to the removal of nonlinear interactions is localized to specific anatomical regions of no interest or to specific principal components. We denoised the data 8 separate times by regressing out 5 principal components extracted from combined white matter (WM) and cerebrospinal fluid (CSF), each of the 5 components separately, 5 components extracted from WM only, and 5 components extracted solely from CSF. In all cases, denoising was sufficient to remove the observed nonlinear interactions.