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  • Neuroinformatics

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  • Open Access
    Authors: 
    Jörn Diedrichsen; Tobias Wiestler; Naveed Ejaz;
    Publisher: Elsevier BV
    Country: Canada
    Project: WT | Learning and recovery of ... (094874), WT | Core support for the Well... (091593)

    How do populations of neurons represent a variable of interest? The notion of feature spaces is a useful concept to approach this question: According to this model, the activation patterns across a neuronal population are composed of different pattern components. The strength of each of these components varies with one latent feature, which together are the dimensions along which the population represents the variable. Here we propose a new method to determine the number of feature dimensions that best describes the activation patterns. The method is based on Gaussian linear classifiers that use only the first d most important pattern dimensions. Using cross-validation, we can identify the classifier that best matches the dimensionality of the neuronal representation. We test this method on two datasets of motor cortical activation patterns measured with functional magnetic resonance imaging (fMRI), during (i) simultaneous presses of all fingers of a hand at different force levels and (ii) presses of different individual fingers at a single force level. As expected, the new method shows that the representation of force is low-dimensional; the neural activation for different force levels is scaled versions of each other. In comparison, individual finger presses are represented in a full, four-dimensional feature space. The approach can be used to determine an important characteristic of neuronal population codes without knowing the form of the underlying features. It therefore provides a novel tool in the building of quantitative models of neuronal population activity as measured with fMRI or other approaches. Highlights • Neural population activity represents external variables using multiple feature dimensions. • We present a general method to determine the dimensionality of this feature space. • Primary motor cortex represents force through a low-dimensional feature space. • Individual finger movements are represented using a four-dimensional feature space.

  • Open Access
    Authors: 
    Culham, Jody;
    Publisher: Scholarship@Western
    Country: Canada

    Neuroimaging, particularly functional magnetic resonance imaging, has identified and characterized many areas of the human cerebral cortex involved in planning and executing body movements. Motor commands are sent to the spinal cord from primary motor cortex, which receives input and modulation from secondary motor cortex, parietal cortex, and prefrontal cortex.

  • Publication . Article . Other literature type . 2005
    Open Access English
    Authors: 
    Michele L. Ries; Taylor W. Schmitz; Tisha N. Kawahara; Britta M. Torgerson; Mehul A. Trivedi; Sterling C. Johnson;
    Country: Canada

    Neuroimaging research has demonstrated that the posterior cingulate cortex (PCC) is functionally compromised in individuals diagnosed with amnestic Mild Cognitive Impairment (MCI), a major risk factor for the development of Alzheimer’s disease (AD). In functional magnetic resonance imaging (fMRI) studies with healthy participants, this same region is active during self-appraisal (requiring retrieval of semantic knowledge about the self) as well as episodic recognition of recently-learned information. Administering both types of tasks to people with MCI may reveal important information regarding the role of the PCC in recollection. This study investigated fMRI activation in the PCC in individuals with MCI and age, gender, and education-matched controls across two tasks. The first task was a visual episodic recognition task in which participants indicated whether pictures had or had not been presented during a study session. The second task was an autobiographical self-appraisal task in which subjects rated themselves on a set of trait adjectives. Results of a conjunction analysis revealed the PCC as the sole region commonly active during both tasks in the healthy older adults. Furthermore, additional analysis revealed an interaction in the PCC indicating a task-dependent response in the MCI group. MCI participants showed PCC activation during self-appraisal, but not during episodic retrieval. These results suggest in MCI that the PCC shows functional degradation during episodic retrieval of visual information learned in the laboratory. In contrast, the PCC’s role in retrieval and evaluation of highly-elaborated information regarding the self is more well-preserved.

  • Open Access English
    Authors: 
    Eva Berlot; Nicola J. Popp; Jörn Diedrichsen;
    Publisher: eLife Sciences Publications, Ltd
    Country: Canada
    Project: NSERC

    eLife digest It has famously been claimed that it takes 10,000 hours to become an expert at something. But while most of us will never become concert pianists, we can all learn new motor skills and improve existing ones – from touch-typing to tennis – by practicing. What happens in the brain to produce these improvements in performance? Researchers have tried to answer this question by scanning the brains of people as they practice motor skills, but the results have proved inconsistent. Some studies find that specific brain areas become more active as people practice. This could indicate that these areas are ‘storing’ new skills. But others report that brain activity decreases with practice. This might indicate that practice instead makes certain brain areas work more efficiently. It is also unclear where in the brain these learning-related changes occur. Some studies suggest that most occur in the primary motor cortex, or M1 – the area that sends commands to muscles. Others suggest that most changes take place outside of M1, in areas that plan movements. Berlot et al. set out to resolve these inconsistencies by scanning the brains of healthy volunteers as they learned to play six 9-digit sequences on a keyboard. Each volunteer completed about 4,000 training trials over 5 weeks, and had their brain scanned four times. As the weeks passed, the volunteers became faster and more accurate at playing the sequences. However, the activity of their primary motor cortex did not change. By contrast, the activity of areas involved in planning movements decreased throughout training. The patterns of activity for each individual sequence reorganized throughout learning in the areas outside of the M1. This happened most quickly during the early stages of training when the volunteers showed the fastest improvements in performance. Overall, these findings suggest that when we learn a new skill, activity in the brain areas supporting that skill decrease as the brain becomes more efficient. Increases in brain activity are thus unlikely to reflect the acquired skill. Instead, more subtle changes, in which the brain uses more specific patterns of activity to encode the skill, may underlie improved performance. This may also be true for other types of learning, such as acquiring a new language. Despite numerous studies, there is little agreement about what brain changes accompany motor sequence learning, partly because of a general publication bias that favors novel results. We therefore decided to systematically reinvestigate proposed functional magnetic resonance imaging correlates of motor learning in a preregistered longitudinal study with four scanning sessions over 5 weeks of training. Activation decreased more for trained than untrained sequences in premotor and parietal areas, without any evidence of learning-related activation increases. Premotor and parietal regions also exhibited changes in the fine-grained, sequence-specific activation patterns early in learning, which stabilized later. No changes were observed in the primary motor cortex (M1). Overall, our study provides evidence that human motor sequence learning occurs outside of M1. Furthermore, it shows that we cannot expect to find activity increases as an indicator for learning, making subtle changes in activity patterns across weeks the most promising fMRI correlate of training-induced plasticity.

  • Open Access
    Authors: 
    John Kounios; Deborah Green; Lisa Payne; Jessica I. Fleck; Ray Grondin; Ken McRae;
    Publisher: Scholarship@Western
    Country: Canada

    Semantic richness refers to the amount of semantic information associated with a concept. Reaction-time (RT) studies have shown that words referring to rich concepts elicit faster responses than those referring to impoverished ones, suggesting that richer concepts are activated more quickly. In a recent functional neuroimaging study, richer concepts evoked less neural activity, which was interpreted as faster activation. The interpretations of these findings appear to conflict with event-related potential (ERP) studies showing no evidence that speed of concept activation is influenced by typical semantic variables. Resolution of this apparent contradiction is important because the interpretation of 40 years of semantic- memory RT studies depends on whether factors such as semantic richness influence the duration of initial concept activation or later decision and response processes. Consistent with previous studies of the effects of semantic factors on ERP, the present study shows that richness influences the magnitude, but not the latency, of the P2 and N400 ERP components (which are early relative to behavioral responses), suggesting that effects of richness on RT reflect temporal effects on downstream decision or response mechanisms rather than on upstream concept activation.

  • Open Access
    Authors: 
    Björn Herrmann; Chad Buckland; Ingrid S. Johnsrude;
    Publisher: Elsevier BV
    Country: Canada
    Project: CIHR

    © 2019 Elsevier Inc. Sensitivity to temporal regularity (e.g., recurring modulation in amplitude) is crucial for speech perception. Degradation of the auditory periphery due to aging and hearing loss may lead to increased responsiveness to sound in the auditory cortex, with potential consequences for the processing of temporal regularities. We used electroencephalography recorded from younger (19–33 years) and older adults (55–76 years) to investigate whether younger and older listeners differ in responsiveness to sound and sensitivity to amplitude modulation in sounds. Aging was associated with reduced adaptation in the auditory cortex, suggesting an age-related increase in responsiveness. Furthermore, neural synchronization in the auditory cortex to 4-Hz amplitude-modulated narrow-band noises was enhanced in ∼30% of older individuals. Despite enhanced responsiveness and synchronization in the auditory cortex, sustained neural activity (likely involving auditory and higher-order regions) in response to amplitude modulation was absent in older people. Aging appears to be associated with over-responsiveness to amplitude modulation in the auditory cortex, but with diminished regularity sensitivity in higher-order areas.

  • Open Access
    Authors: 
    Hutchison, R. Matthew; Morton, J. Bruce;
    Publisher: Scholarship@Western
    Country: Canada

    Cognitive control is a process that unfolds over time and regulates thought and action in the service of achieving goals and managing unanticipated challenges. Prevailing accounts attribute the protracted development of this mental process to incremental changes in the functional organization of a cognitive control network. Here, we challenge the notion that cognitive control is linked to a topologically static network, and argue that the capacity to manage unanticipated challenges and its development should instead be characterized in terms of inter-regional functional coupling dynamics. Ongoing changes in temporal coupling have long represented a fundamental pillar in both empirical and theoretical-based accounts of brain function, but have been largely ignored by traditional neuroimaging methods that assume a fixed functional architecture. There is, however, a growing recognition of the importance of temporal coupling dynamics for brain function, and this has led to rapid innovations in analytic methods. Results in this new frontier of neuroimaging suggest that time-varying changes in connectivity strength and direction exist at the large scale and further, that network patterns, like cognitive control process themselves, are transient and dynamic.

  • Open Access English
    Authors: 
    Kelly Shen; Gleb Bezgin; Michael Schirner; Petra Ritter; Stefan Everling; Anthony R. McIntosh;
    Publisher: Nature Publishing Group UK
    Country: Canada
    Project: EC | BrainModes (683049), EC | HBP SGA2 (785907), EC | VirtualBrainCloud (826421), CIHR

    Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data. Design Type(s)data integration objective • source-based data transformation objective • modeling and simulation objectiveMeasurement Type(s)brain measurementTechnology Type(s)functional magnetic resonance imaging • Diffusion Weighted ImagingFactor Type(s)Sample Characteristic(s)Macaca • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)

  • Publication . Other literature type . Article . 2016
    Open Access
    Authors: 
    David J. Ostry; Paul L. Gribble;
    Publisher: Elsevier BV
    Country: Canada
    Project: NSERC

    There is accumulating evidence from behavioral, neurophysiological, and neuroimaging studies that the acquisition of motor skills involves both perceptual and motor learning. Perceptual learning alters movements, motor learning, and motor networks of the brain. Motor learning changes perceptual function and the sensory circuits of the brain. Here, we review studies of both human limb movement and speech that indicate that plasticity in sensory and motor systems is reciprocally linked. Taken together, this points to an approach to motor learning in which perceptual learning and sensory plasticity have a fundamental role.

  • Open Access English
    Authors: 
    Cédric Annweiler; Manuel Montero-Odasso; Susan W Muir; Olivier Beauchet;
    Publisher: HAL CCSD
    Countries: France, Canada
    Project: CIHR

    International audience; Hypovitaminosis D is associated with cognitive decline in the elderly, but the issue of causality remains unresolved. Definitive evidence would include the visualization of brain lesions resulting from hypovitaminosis D. The aim of the present article is to determine, through a literature review, the location and nature of possible brain disorders in hypovitaminosis D. We found limited brain-imaging data, which reported ischemic infarcts and white matter hyperintensities in hypovitaminosis D, though did not provide their specific location or report any focal atrophy. Based on the finding of executive dysfunctions (i.e., mental shifting and information updating impairments) in the presence of hypovitaminosis D, we suggest that hypovitaminosis D is associated with a dysfunction of the frontal-subcortical neuronal circuits, particularly the dorsolateral circuit. Further imaging studies are required to corroborate this assumption and to determine whether hypovitaminosis D results in degenerative and / or vascular lesions.

search
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
595 Research products, page 1 of 60
  • Open Access
    Authors: 
    Jörn Diedrichsen; Tobias Wiestler; Naveed Ejaz;
    Publisher: Elsevier BV
    Country: Canada
    Project: WT | Learning and recovery of ... (094874), WT | Core support for the Well... (091593)

    How do populations of neurons represent a variable of interest? The notion of feature spaces is a useful concept to approach this question: According to this model, the activation patterns across a neuronal population are composed of different pattern components. The strength of each of these components varies with one latent feature, which together are the dimensions along which the population represents the variable. Here we propose a new method to determine the number of feature dimensions that best describes the activation patterns. The method is based on Gaussian linear classifiers that use only the first d most important pattern dimensions. Using cross-validation, we can identify the classifier that best matches the dimensionality of the neuronal representation. We test this method on two datasets of motor cortical activation patterns measured with functional magnetic resonance imaging (fMRI), during (i) simultaneous presses of all fingers of a hand at different force levels and (ii) presses of different individual fingers at a single force level. As expected, the new method shows that the representation of force is low-dimensional; the neural activation for different force levels is scaled versions of each other. In comparison, individual finger presses are represented in a full, four-dimensional feature space. The approach can be used to determine an important characteristic of neuronal population codes without knowing the form of the underlying features. It therefore provides a novel tool in the building of quantitative models of neuronal population activity as measured with fMRI or other approaches. Highlights • Neural population activity represents external variables using multiple feature dimensions. • We present a general method to determine the dimensionality of this feature space. • Primary motor cortex represents force through a low-dimensional feature space. • Individual finger movements are represented using a four-dimensional feature space.

  • Open Access
    Authors: 
    Culham, Jody;
    Publisher: Scholarship@Western
    Country: Canada

    Neuroimaging, particularly functional magnetic resonance imaging, has identified and characterized many areas of the human cerebral cortex involved in planning and executing body movements. Motor commands are sent to the spinal cord from primary motor cortex, which receives input and modulation from secondary motor cortex, parietal cortex, and prefrontal cortex.

  • Publication . Article . Other literature type . 2005
    Open Access English
    Authors: 
    Michele L. Ries; Taylor W. Schmitz; Tisha N. Kawahara; Britta M. Torgerson; Mehul A. Trivedi; Sterling C. Johnson;
    Country: Canada

    Neuroimaging research has demonstrated that the posterior cingulate cortex (PCC) is functionally compromised in individuals diagnosed with amnestic Mild Cognitive Impairment (MCI), a major risk factor for the development of Alzheimer’s disease (AD). In functional magnetic resonance imaging (fMRI) studies with healthy participants, this same region is active during self-appraisal (requiring retrieval of semantic knowledge about the self) as well as episodic recognition of recently-learned information. Administering both types of tasks to people with MCI may reveal important information regarding the role of the PCC in recollection. This study investigated fMRI activation in the PCC in individuals with MCI and age, gender, and education-matched controls across two tasks. The first task was a visual episodic recognition task in which participants indicated whether pictures had or had not been presented during a study session. The second task was an autobiographical self-appraisal task in which subjects rated themselves on a set of trait adjectives. Results of a conjunction analysis revealed the PCC as the sole region commonly active during both tasks in the healthy older adults. Furthermore, additional analysis revealed an interaction in the PCC indicating a task-dependent response in the MCI group. MCI participants showed PCC activation during self-appraisal, but not during episodic retrieval. These results suggest in MCI that the PCC shows functional degradation during episodic retrieval of visual information learned in the laboratory. In contrast, the PCC’s role in retrieval and evaluation of highly-elaborated information regarding the self is more well-preserved.

  • Open Access English
    Authors: 
    Eva Berlot; Nicola J. Popp; Jörn Diedrichsen;
    Publisher: eLife Sciences Publications, Ltd
    Country: Canada
    Project: NSERC

    eLife digest It has famously been claimed that it takes 10,000 hours to become an expert at something. But while most of us will never become concert pianists, we can all learn new motor skills and improve existing ones – from touch-typing to tennis – by practicing. What happens in the brain to produce these improvements in performance? Researchers have tried to answer this question by scanning the brains of people as they practice motor skills, but the results have proved inconsistent. Some studies find that specific brain areas become more active as people practice. This could indicate that these areas are ‘storing’ new skills. But others report that brain activity decreases with practice. This might indicate that practice instead makes certain brain areas work more efficiently. It is also unclear where in the brain these learning-related changes occur. Some studies suggest that most occur in the primary motor cortex, or M1 – the area that sends commands to muscles. Others suggest that most changes take place outside of M1, in areas that plan movements. Berlot et al. set out to resolve these inconsistencies by scanning the brains of healthy volunteers as they learned to play six 9-digit sequences on a keyboard. Each volunteer completed about 4,000 training trials over 5 weeks, and had their brain scanned four times. As the weeks passed, the volunteers became faster and more accurate at playing the sequences. However, the activity of their primary motor cortex did not change. By contrast, the activity of areas involved in planning movements decreased throughout training. The patterns of activity for each individual sequence reorganized throughout learning in the areas outside of the M1. This happened most quickly during the early stages of training when the volunteers showed the fastest improvements in performance. Overall, these findings suggest that when we learn a new skill, activity in the brain areas supporting that skill decrease as the brain becomes more efficient. Increases in brain activity are thus unlikely to reflect the acquired skill. Instead, more subtle changes, in which the brain uses more specific patterns of activity to encode the skill, may underlie improved performance. This may also be true for other types of learning, such as acquiring a new language. Despite numerous studies, there is little agreement about what brain changes accompany motor sequence learning, partly because of a general publication bias that favors novel results. We therefore decided to systematically reinvestigate proposed functional magnetic resonance imaging correlates of motor learning in a preregistered longitudinal study with four scanning sessions over 5 weeks of training. Activation decreased more for trained than untrained sequences in premotor and parietal areas, without any evidence of learning-related activation increases. Premotor and parietal regions also exhibited changes in the fine-grained, sequence-specific activation patterns early in learning, which stabilized later. No changes were observed in the primary motor cortex (M1). Overall, our study provides evidence that human motor sequence learning occurs outside of M1. Furthermore, it shows that we cannot expect to find activity increases as an indicator for learning, making subtle changes in activity patterns across weeks the most promising fMRI correlate of training-induced plasticity.

  • Open Access
    Authors: 
    John Kounios; Deborah Green; Lisa Payne; Jessica I. Fleck; Ray Grondin; Ken McRae;
    Publisher: Scholarship@Western
    Country: Canada

    Semantic richness refers to the amount of semantic information associated with a concept. Reaction-time (RT) studies have shown that words referring to rich concepts elicit faster responses than those referring to impoverished ones, suggesting that richer concepts are activated more quickly. In a recent functional neuroimaging study, richer concepts evoked less neural activity, which was interpreted as faster activation. The interpretations of these findings appear to conflict with event-related potential (ERP) studies showing no evidence that speed of concept activation is influenced by typical semantic variables. Resolution of this apparent contradiction is important because the interpretation of 40 years of semantic- memory RT studies depends on whether factors such as semantic richness influence the duration of initial concept activation or later decision and response processes. Consistent with previous studies of the effects of semantic factors on ERP, the present study shows that richness influences the magnitude, but not the latency, of the P2 and N400 ERP components (which are early relative to behavioral responses), suggesting that effects of richness on RT reflect temporal effects on downstream decision or response mechanisms rather than on upstream concept activation.

  • Open Access
    Authors: 
    Björn Herrmann; Chad Buckland; Ingrid S. Johnsrude;
    Publisher: Elsevier BV
    Country: Canada
    Project: CIHR

    © 2019 Elsevier Inc. Sensitivity to temporal regularity (e.g., recurring modulation in amplitude) is crucial for speech perception. Degradation of the auditory periphery due to aging and hearing loss may lead to increased responsiveness to sound in the auditory cortex, with potential consequences for the processing of temporal regularities. We used electroencephalography recorded from younger (19–33 years) and older adults (55–76 years) to investigate whether younger and older listeners differ in responsiveness to sound and sensitivity to amplitude modulation in sounds. Aging was associated with reduced adaptation in the auditory cortex, suggesting an age-related increase in responsiveness. Furthermore, neural synchronization in the auditory cortex to 4-Hz amplitude-modulated narrow-band noises was enhanced in ∼30% of older individuals. Despite enhanced responsiveness and synchronization in the auditory cortex, sustained neural activity (likely involving auditory and higher-order regions) in response to amplitude modulation was absent in older people. Aging appears to be associated with over-responsiveness to amplitude modulation in the auditory cortex, but with diminished regularity sensitivity in higher-order areas.

  • Open Access
    Authors: 
    Hutchison, R. Matthew; Morton, J. Bruce;
    Publisher: Scholarship@Western
    Country: Canada

    Cognitive control is a process that unfolds over time and regulates thought and action in the service of achieving goals and managing unanticipated challenges. Prevailing accounts attribute the protracted development of this mental process to incremental changes in the functional organization of a cognitive control network. Here, we challenge the notion that cognitive control is linked to a topologically static network, and argue that the capacity to manage unanticipated challenges and its development should instead be characterized in terms of inter-regional functional coupling dynamics. Ongoing changes in temporal coupling have long represented a fundamental pillar in both empirical and theoretical-based accounts of brain function, but have been largely ignored by traditional neuroimaging methods that assume a fixed functional architecture. There is, however, a growing recognition of the importance of temporal coupling dynamics for brain function, and this has led to rapid innovations in analytic methods. Results in this new frontier of neuroimaging suggest that time-varying changes in connectivity strength and direction exist at the large scale and further, that network patterns, like cognitive control process themselves, are transient and dynamic.

  • Open Access English
    Authors: 
    Kelly Shen; Gleb Bezgin; Michael Schirner; Petra Ritter; Stefan Everling; Anthony R. McIntosh;
    Publisher: Nature Publishing Group UK
    Country: Canada
    Project: EC | BrainModes (683049), EC | HBP SGA2 (785907), EC | VirtualBrainCloud (826421), CIHR

    Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data. Design Type(s)data integration objective • source-based data transformation objective • modeling and simulation objectiveMeasurement Type(s)brain measurementTechnology Type(s)functional magnetic resonance imaging • Diffusion Weighted ImagingFactor Type(s)Sample Characteristic(s)Macaca • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)

  • Publication . Other literature type . Article . 2016
    Open Access
    Authors: 
    David J. Ostry; Paul L. Gribble;
    Publisher: Elsevier BV
    Country: Canada
    Project: NSERC

    There is accumulating evidence from behavioral, neurophysiological, and neuroimaging studies that the acquisition of motor skills involves both perceptual and motor learning. Perceptual learning alters movements, motor learning, and motor networks of the brain. Motor learning changes perceptual function and the sensory circuits of the brain. Here, we review studies of both human limb movement and speech that indicate that plasticity in sensory and motor systems is reciprocally linked. Taken together, this points to an approach to motor learning in which perceptual learning and sensory plasticity have a fundamental role.

  • Open Access English
    Authors: 
    Cédric Annweiler; Manuel Montero-Odasso; Susan W Muir; Olivier Beauchet;
    Publisher: HAL CCSD
    Countries: France, Canada
    Project: CIHR

    International audience; Hypovitaminosis D is associated with cognitive decline in the elderly, but the issue of causality remains unresolved. Definitive evidence would include the visualization of brain lesions resulting from hypovitaminosis D. The aim of the present article is to determine, through a literature review, the location and nature of possible brain disorders in hypovitaminosis D. We found limited brain-imaging data, which reported ischemic infarcts and white matter hyperintensities in hypovitaminosis D, though did not provide their specific location or report any focal atrophy. Based on the finding of executive dysfunctions (i.e., mental shifting and information updating impairments) in the presence of hypovitaminosis D, we suggest that hypovitaminosis D is associated with a dysfunction of the frontal-subcortical neuronal circuits, particularly the dorsolateral circuit. Further imaging studies are required to corroborate this assumption and to determine whether hypovitaminosis D results in degenerative and / or vascular lesions.