An evaluation of the use of multidimensional scaling for understanding brain connectivity

A large amount of data is now available about the pattern of connections between brain regions. Computational methods are increasingly relevant for uncovering structure in such datasets. There has been recent interest in the use of non-metric multidimensional scaling (nmds) for such analysis, nmds produces a spatial representation of the ‘dissimilarities’ between a number of entities. Normally, it is applied to data matrices containing a large number of levels of dissimilarity, whereas for brain connectivity data there is a very small number. We address the suitability of nmds for this case. Systematic numerical studies are presented to evaluate the ability of this method to reconstruct known geometrical configurations from dissimilarity data possessing few levels. In this case there is a strong bias for nmds to produce annular configurations, whether or not such structure exists in the original data. For the case of a connectivity dataset derived from the primate cortical visual system, we demonstrate that great caution is needed in interpreting the resulting configuration. Application of an independent method that we developed also strongly suggests that the visual system nmds configuration is affected by an annular bias. We question the strength of support that an nmds analysis of the visual system data provides for the two streams view of visual processing.

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Glutamate in the pathophysiology of schizophrenia

Psychotic Disorders ◽

10.1093/med/9780190653279.003.0032 ◽

2020 ◽

pp. 287-296

Author(s):

Daniel C. Javitt

Keyword(s):

Visual System ◽

Visual Processing ◽

Negative Symptoms ◽

Glutamate Release ◽

Critical Role ◽

Brain Regions ◽

Long Term Memory ◽

Subcortical Structures ◽

Impaired Metabolism

Glutamate theories of schizophrenia were first proposed over 30 years ago and since that time have become increasingly accepted. Theories are supported by the ability of N-methyl-D-aspartate receptor (NMDAR) antagonists such as phencyclidine (PCP) or ketamine to induce symptoms that closely resemble those of schizophrenia. Moreover, NMDAR antagonists uniquely reproduce the level of negative symptoms and cognitive deficits observed in schizophrenia, suggesting that such models may be particularly appropriate to poor outcome forms of the disorder. As opposed to dopamine, which is most prominent within frontostriatal brain regions, glutamate neurons are present throughout cortex and subcortical structures. Thus, NMDAR theories predict widespread disturbances across cortical and thalamic pathways, including sensory brain regions. In auditory cortex, NMDAR play a critical role in the generation of mismatch negativity (MMN), which may therefore serve as a translational marker of NMDAR dysfunction across species. In the visual system, NMDAR play a critical role in function of the magnocellular visual system. Deficits in both auditory and visual processing contribute to social and communication deficits, which, in turn, lead to poor functional outcome. By contrast, NMDAR dysfunction within the frontohippocampal system may contribute to well described deficits in working memory, executive processing and long-term memory formation. Deficits in NMDAR function may be driven by disturbances in presynaptic glutamate release, impaired metabolism of NMDAR modulators such as glycine or D-serine, or intrinsic abnormalities in NMDAR themselves.

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Color signals through dorsal and ventral visual pathways

Visual Neuroscience ◽

10.1017/s0952523813000382 ◽

2013 ◽

Vol 31 (2) ◽

pp. 197-209 ◽

Cited By ~ 36

Author(s):

BEVIL R. CONWAY

Keyword(s):

Visual Processing ◽

Color Space ◽

Temporal Cortex ◽

Color Perception ◽

Original Data ◽

Brain Regions ◽

Neural Representation ◽

Inferior Temporal Cortex ◽

Extrastriate Cortex ◽

Chromatic Contrast

AbstractExplanations for color phenomena are often sought in the retina, lateral geniculate nucleus, and V1, yet it is becoming increasingly clear that a complete account will take us further along the visual-processing pathway. Working out which areas are involved is not trivial. Responses to S-cone activation are often assumed to indicate that an area or neuron is involved in color perception. However, work tracing S-cone signals into extrastriate cortex has challenged this assumption: S-cone responses have been found in brain regions, such as the middle temporal (MT) motion area, not thought to play a major role in color perception. Here, we review the processing of S-cone signals across cortex and present original data on S-cone responses measured with fMRI in alert macaque, focusing on one area in which S-cone signals seem likely to contribute to color (V4/posterior inferior temporal cortex) and on one area in which S signals are unlikely to play a role in color (MT). We advance a hypothesis that the S-cone signals in color-computing areas are required to achieve a balanced neural representation of perceptual color space, whereas those in noncolor-areas provide a cue to illumination (not luminance) and confer sensitivity to the chromatic contrast generated by natural daylight (shadows, illuminated by ambient sky, surrounded by direct sunlight). This sensitivity would facilitate the extraction of shape-from-shadow signals to benefit global scene analysis and motion perception.

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Non-metric multidimensional scaling in the analysis of neuroanatomical connection data and the organization of the primate cortical visual system

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.1995.0069 ◽

1995 ◽

Vol 348 (1325) ◽

pp. 281-308 ◽

Cited By ~ 55

Keyword(s):

Central Nervous System ◽

Nervous System ◽

Visual System ◽

Multidimensional Scaling ◽

Artificial Data ◽

Level Data ◽

The Central Nervous System ◽

Methodological Aspects ◽

The Brain ◽

Nmds Analysis

Neuroanatomists have established that the various gross structures of the brain are divided into a large number of different processing regions and have catalogued a large number of connections between these regions. The connectional data derived from neuroanatomical studies are complex, and reliable conclusions about the organization of brain systems cannot be drawn from considering them without some supporting analysis. Recognition of this problem has recently led to the application of a variety of techniques to the analysis of connection data. One of the techniques that we previously employed, nonmetric multidimensional scaling (nmds), appears to have revealed important aspects of the organization of the central nervous system, such as the gross organization of the whole cortical network in two species. We present here a detailed treatment of methodological aspects of the application of nmds to connection data. We first examine in detail the particular properties of neuroanatomical connection data. Second, we consider the details of nmds and discuss the propriety of different possible nmds approaches. Third, we present results of the analyses of connection data from the primate visual system, and discuss their interpretation. Fourth, we study independent analyses of the organization of the visual system, and examine the relation between the results of these analyses and those from nmds. Fifth, we investigate quantitatively the performance of a number of data transformation and conditioning procedures, as well as tied and untied nmds analysis of untransformed low-level data, to determine how well nmds can recover known metric parameters from artificial data. We then re-analyse real connectivity data with the most successful methods at removing the effects of sparsity, to ensure that this aspect of data structure does not obscure others. Finally, we summarize the evidence on the connectional organization of the primate visual system, and discuss the reliability of nmds analyses of neuroanatomical connection data.

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Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth

Scientific Reports ◽

10.1038/s41598-021-81681-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marion I. van den Heuvel ◽

Jasmine L. Hect ◽

Benjamin L. Smarr ◽

Tamara Qawasmeh ◽

Lance J. Kriegsfeld ◽

...

Keyword(s):

Prenatal Stress ◽

Sleep Problems ◽

Brain Connectivity ◽

Fetal Brain ◽

Brain Regions ◽

In Utero ◽

Resting State Functional Connectivity ◽

Child Sleep ◽

Maternal Prenatal Stress

AbstractChild sleep disorders are increasingly prevalent and understanding early predictors of sleep problems, starting in utero, may meaningfully guide future prevention efforts. Here, we investigated whether prenatal exposure to maternal psychological stress is associated with increased sleep problems in toddlers. We also examined whether fetal brain connectivity has direct or indirect influence on this putative association. Pregnant women underwent fetal resting-state functional connectivity MRI and completed questionnaires on stress, worry, and negative affect. At 3-year follow-up, 64 mothers reported on child sleep problems, and in the subset that have reached 5-year follow-up, actigraphy data (N = 25) has also been obtained. We observe that higher maternal prenatal stress is associated with increased toddler sleep concerns, with actigraphy sleep metrics, and with decreased fetal cerebellar-insular connectivity. Specific mediating effects were not identified for the fetal brain regions examined. The search for underlying mechanisms of the link between maternal prenatal stress and child sleep problems should be continued and extended to other brain areas.

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Category representations in the brain are both discretely localized and widely distributed

Journal of Neurophysiology ◽

10.1152/jn.00912.2017 ◽

2018 ◽

Vol 119 (6) ◽

pp. 2256-2264 ◽

Cited By ~ 3

Author(s):

Zarrar Shehzad ◽

Gregory McCarthy

Keyword(s):

Functional Mri ◽

Brain Connectivity ◽

Strong Support ◽

Brain Regions ◽

Distributed Representations ◽

Domain Specific ◽

Category Information ◽

Shared Information ◽

Unique Domain ◽

The Brain

Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.

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Visual search for object categories is predicted by the representational architecture of high-level visual cortex

Journal of Neurophysiology ◽

10.1152/jn.00569.2016 ◽

2017 ◽

Vol 117 (1) ◽

pp. 388-402 ◽

Cited By ~ 14

Author(s):

Michael A. Cohen ◽

George A. Alvarez ◽

Ken Nakayama ◽

Talia Konkle

Keyword(s):

Visual Cortex ◽

Visual Search ◽

Visual System ◽

Visual Processing ◽

Search Task ◽

Visual Search Task ◽

Visual Object ◽

Neural Responses ◽

Object Categories ◽

High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

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Increased hypothalamic–pituitary–adrenal drive is associated with decreased appetite and hypoactivation of food-motivation neurocircuitry in anorexia nervosa

Acta Endocrinologica ◽

10.1530/eje-13-0433 ◽

2013 ◽

Vol 169 (5) ◽

pp. 639-647 ◽

Cited By ~ 36

Author(s):

Elizabeth A Lawson ◽

Laura M Holsen ◽

Rebecca DeSanti ◽

McKale Santin ◽

Erinne Meenaghan ◽

...

Keyword(s):

Anorexia Nervosa ◽

Visual Processing ◽

Serum Cortisol ◽

Brain Regions ◽

Cross Sectional Study ◽

Food Motivation ◽

Cross Sectional ◽

Hypothalamic Pituitary Adrenal ◽

Cortisol Levels ◽

The Relationship

ObjectiveCorticotrophin-releasing hormone (CRH)-mediated hypercortisolemia has been demonstrated in anorexia nervosa (AN), a psychiatric disorder characterized by food restriction despite low body weight. While CRH is anorexigenic, downstream cortisol stimulates hunger. Using a food-related functional magnetic resonance imaging (fMRI) paradigm, we have demonstrated hypoactivation of brain regions involved in food motivation in women with AN, even after weight recovery. The relationship between hypothalamic–pituitary–adrenal (HPA) axis dysregulation and appetite and the association with food-motivation neurocircuitry hypoactivation are unknown in AN. We investigated the relationship between HPA activity, appetite, and food-motivation neurocircuitry hypoactivation in AN.DesignCross-sectional study of 36 women (13 AN, ten weight-recovered AN (ANWR), and 13 healthy controls (HC)).MethodsPeripheral cortisol and ACTH levels were measured in a fasting state and 30, 60, and 120 min after a standardized mixed meal. The visual analog scale was used to assess homeostatic and hedonic appetite. fMRI was performed during visual processing of food and non-food stimuli to measure the brain activation pre- and post-meal.ResultsIn each group, serum cortisol levels decreased following the meal. Mean fasting, 120 min post-meal, and nadir cortisol levels were high in AN vs HC. Mean postprandial ACTH levels were high in ANWR compared with HC and AN subjects. Cortisol levels were associated with lower fasting homeostatic and hedonic appetite, independent of BMI and depressive symptoms. Cortisol levels were also associated with between-group variance in activation in the food-motivation brain regions (e.g. hypothalamus, amygdala, hippocampus, orbitofrontal cortex, and insula).ConclusionsHPA activation may contribute to the maintenance of AN by the suppression of appetitive drive.

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Dynamic and stable brain connectivity during movie watching as revealed by functional MRI

10.1101/2021.09.14.460293 ◽

2021 ◽

Author(s):

Xin Di ◽

Zhiguo Zhang ◽

Ting Xu ◽

Bharat B. Biswal

Keyword(s):

Functional Mri ◽

Brain Connectivity ◽

Brain Regions ◽

Posterior Cingulate Cortex ◽

Temporoparietal Junction ◽

Related Information ◽

Movie Clip ◽

Brain Changes ◽

Brain Processing ◽

Dynamic Connectivity

AbstractSpatially remote brain regions show synchronized activity as typically revealed by correlated functional MRI (fMRI) signals. An emerging line of research has focused on the temporal fluctuations of connectivity, however, its relationships with stable connectivity have not been clearly illustrated. We examined the stable and dynamic connectivity from fMRI data when the participants watched four different movie clips. Using inter-individual correlation, we were able to estimate functionally meaningful dynamic connectivity associated with different movies. Widespread consistent dynamic connectivity was observed for each movie clip as well as their differences between clips. A cartoon movie clip showed higher consistent dynamic connectivity with the posterior cingulate cortex and supramarginal gyrus, while a court drama clip showed higher dynamic connectivity with the auditory cortex and temporoparietal junction, which suggest the involvement of specific brain processing for different movie contents. In contrast, stable connectivity was highly similar among the movie clips, and showed fewer statistical significant differences. The patterns of dynamic connectivity had higher accuracy for classifications of different movie clips than the stable connectivity and regional activity. These results support the functional significance of dynamic connectivity in reflecting functional brain changes, which could provide more functionally related information than stable connectivity.

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Changes in functional connectivity associated with facial expression processing over the working adult lifespan

10.31234/osf.io/yrkbw ◽

2021 ◽

Author(s):

Thomas Murray ◽

Justin O'Brien ◽

Veena Kumari

Keyword(s):

Functional Connectivity ◽

Facial Expressions ◽

Brain Connectivity ◽

Negative Emotions ◽

Brain Regions ◽

Emotional Expressions ◽

Expression Recognition ◽

Frontal Pole ◽

Adult Lifespan ◽

Age Related

The recognition of negative emotions from facial expressions is shown to decline across the adult lifespan, with some evidence that this decline begins around middle age. While some studies have suggested ageing may be associated with changes in neural response to emotional expressions, it is not known whether ageing is associated with changes in the network connectivity associated with processing emotional expressions. In this study, we examined the effect of participant age on whole-brain connectivity to various brain regions that have been associated with connectivity during emotion processing: the left and right amygdalae, medial prefrontal cortex (mPFC), and right posterior superior temporal sulcus (rpSTS). The study involved healthy participants aged 20-65 who viewed facial expressions displaying anger, fear, happiness, and neutral expressions during functional magnetic resonance imaging (fMRI). We found effects of age on connectivity between the left amygdala and voxels in the occipital pole and cerebellum, between the right amygdala and voxels in the frontal pole, and between the rpSTS and voxels in the orbitofrontal cortex, but no effect of age on connectivity with the mPFC. Furthermore, ageing was more greatly associated with a decline in connectivity to the left amygdala and rpSTS for negative expressions in comparison to happy and neutral expressions, consistent with the literature suggesting a specific age-related decline in the recognition of negative emotions. These results add to the literature surrounding ageing and expression recognition by suggesting that changes in underlying functional connectivity might contribute to changes in recognition of negative facial expressions across the adult lifespan.

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Spatial Patterns for Discriminative Estimation

10.1101/746891 ◽

2019 ◽

Author(s):

Alberto Llera ◽

Roselyne Chauvin ◽

Peter Mulders ◽

Jilly Naaijen ◽

Maarten Mennes ◽

...

Keyword(s):

Spatial Patterns ◽

Brain Connectivity ◽

Covariance Structure ◽

Brain Regions ◽

Linear Filters ◽

Cognitive Changes ◽

Experimental Conditions ◽

Cognitive States ◽

Human Connectome Project ◽

Perfect Discrimination

AbstractFunctional connectivity between brain regions is modulated by cognitive states or experimental conditions. A multivariate methodology that can capture fMRI connectivity maps in light of different experimental conditions would be of primary importance to learn about the specific roles of the different brain areas involved in the observed connectivity variations. Here we detail, adapt, optimize and evaluate a supervised dimensionality reduction model to fMRI timeseries. We demonstrate the strength of such an approach for fMRI data using data from the Human Connectome Project to show that the model provides close to perfect discrimination between different fMRI tasks at low dimensionality. The straightforward interpretability and relevance of the model results is demonstrated by the obtained linear filters relating to anatomical areas well known to be involved on each considered task, and its robustness by testing discriminatory generalization and spatial reproducibility with respect to the number of subjects and fMRI time-points acquired. We additionally suggest how such approach can provide a complementary view to traditional task fMRI analyses by looking at changes in the covariance structure as a substitute to changes in the mean signal. We conclude that the presented methodology provides a robust tool to investigate brain connectivity alterations across induced cognitive changes and has the potential to be used in pathological or pharmacological cohort studies. A publicly available toolbox is provided to facilitate the end use and further development of this methodology to extract Spatial Patterns for Discriminative Estimation (SP♠DE).

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