Brain Anatomy and Auditory Physiology

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

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Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

Journal of Digital Imaging ◽

10.1007/s10278-020-00400-7 ◽

2021 ◽

Author(s):

Pooja Prabhu ◽

A. K. Karunakar ◽

Sanjib Sinha ◽

N. Mariyappa ◽

G. K. Bhargava ◽

...

Keyword(s):

Large Scale ◽

Brain Mri ◽

Similarity Measures ◽

Principal Component ◽

Brain Anatomy ◽

Morphological Operations ◽

Mr Images ◽

Tilt Correction ◽

Brain Mr Images ◽

The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

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Effects of eight neuropsychiatric copy number variants on human brain structure

Translational Psychiatry ◽

10.1038/s41398-021-01490-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Claudia Modenato ◽

Kuldeep Kumar ◽

Clara Moreau ◽

Sandra Martin-Brevet ◽

Guillaume Huguet ◽

...

Keyword(s):

Copy Number ◽

Copy Number Variants ◽

Principal Component ◽

Brain Morphology ◽

Brain Anatomy ◽

Brain Morphometry ◽

Supplementary Motor Cortex ◽

Psychiatric Conditions ◽

Latent Dimension ◽

Mirror Effects

AbstractMany copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions.

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Brain morphology predicts social intelligence in wild cleaner fish

Nature Communications ◽

10.1038/s41467-020-20130-2 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Zegni Triki ◽

Yasmin Emery ◽

Magda C. Teles ◽

Rui F. Oliveira ◽

Redouan Bshary

Keyword(s):

Social Intelligence ◽

Brain Plasticity ◽

Decision Rules ◽

Brain Morphology ◽

Brain Anatomy ◽

Environmental Complexity ◽

Learning Abilities ◽

Cleaner Fish ◽

High Performing ◽

Selective Pressures

AbstractIt is generally agreed that variation in social and/or environmental complexity yields variation in selective pressures on brain anatomy, where more complex brains should yield increased intelligence. While these insights are based on many evolutionary studies, it remains unclear how ecology impacts brain plasticity and subsequently cognitive performance within a species. Here, we show that in wild cleaner fish (Labroides dimidiatus), forebrain size of high-performing individuals tested in an ephemeral reward task covaried positively with cleaner density, while cerebellum size covaried negatively with cleaner density. This unexpected relationship may be explained if we consider that performance in this task reflects the decision rules that individuals use in nature rather than learning abilities: cleaners with relatively larger forebrains used decision-rules that appeared to be locally optimal. Thus, social competence seems to be a suitable proxy of intelligence to understand individual differences under natural conditions.

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Clinical and anatomical heterogeneity in autistic spectrum disorder: a structural MRI study

Psychological Medicine ◽

10.1017/s0033291709991541 ◽

2009 ◽

Vol 40 (7) ◽

pp. 1171-1181 ◽

Cited By ~ 70

Author(s):

F. Toal ◽

E. M. Daly ◽

L. Page ◽

Q. Deeley ◽

B. Hallahan ◽

...

Keyword(s):

Asperger Syndrome ◽

Autistic Spectrum Disorder ◽

Grey Matter ◽

Clinical Phenotype ◽

Brain Regions ◽

Spectrum Disorder ◽

Grey Matter Volume ◽

Brain Anatomy ◽

Autistic Spectrum ◽

Adults With Asd

BackgroundAutistic spectrum disorder (ASD) is characterized by stereotyped/obsessional behaviours and social and communicative deficits. However, there is significant variability in the clinical phenotype; for example, people with autism exhibit language delay whereas those with Asperger syndrome do not. It remains unclear whether localized differences in brain anatomy are associated with variation in the clinical phenotype.MethodWe used voxel-based morphometry (VBM) to investigate brain anatomy in adults with ASD. We included 65 adults diagnosed with ASD (39 with Asperger syndrome and 26 with autism) and 33 controls who did not differ significantly in age or gender.ResultsVBM revealed that subjects with ASD had a significant reduction in grey-matter volume of medial temporal, fusiform and cerebellar regions, and in white matter of the brainstem and cerebellar regions. Furthermore, within the subjects with ASD, brain anatomy varied with clinical phenotype. Those with autism demonstrated an increase in grey matter in frontal and temporal lobe regions that was not present in those with Asperger syndrome.ConclusionsAdults with ASD have significant differences from controls in the anatomy of brain regions implicated in behaviours characterizing the disorder, and this differs according to clinical subtype.

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Dissecting the midlife crisis: disentangling social, personality and demographic determinants in social brain anatomy

Communications Biology ◽

10.1038/s42003-021-02206-x ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Hannah Kiesow ◽

Lucina Q. Uddin ◽

Boris C. Bernhardt ◽

Joseph Kable ◽

Danilo Bzdok

Keyword(s):

Brain Regions ◽

Interindividual Variation ◽

Brain Morphology ◽

Brain Anatomy ◽

Leadership Roles ◽

Social Brain ◽

Aging Parents ◽

Health Satisfaction ◽

Midlife Transitions ◽

Fully Bayesian

AbstractIn any stage of life, humans crave connection with other people. In midlife, transitions in social networks can relate to new leadership roles at work or becoming a caregiver for aging parents. Previous neuroimaging studies have pinpointed the medial prefrontal cortex (mPFC) to undergo structural remodelling during midlife. Social behavior, personality predisposition, and demographic profile all have intimate links to the mPFC according in largely disconnected literatures. Here, we explicitly estimated their unique associations with brain structure using a fully Bayesian framework. We weighed against each other a rich collection of 40 UK Biobank traits with their interindividual variation in social brain morphology in ~10,000 middle-aged participants. Household size and daily routines showed several of the largest effects in explaining variation in social brain regions. We also revealed male-biased effects in the dorsal mPFC and amygdala for job income, and a female-biased effect in the ventral mPFC for health satisfaction.

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What have lizard ears taught us about auditory physiology?

Hearing Research ◽

10.1016/j.heares.2007.09.011 ◽

2008 ◽

Vol 238 (1-2) ◽

pp. 3-11 ◽

Cited By ~ 25

Author(s):

Geoffrey A. Manley ◽

Christine Köppl

Keyword(s):

Auditory Physiology

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