A neuroimaging investigation of the association between aerobic fitness, hippocampal volume and memory performance in preadolescent children

Because children are becoming increasingly overweight, unhealthy and unfit, understanding the neurocognitive benefits of an active lifestyle in childhood has important public health and educational implications. Animal research has indicated that aerobic exercise is related to increased cell proliferation and survival in the hippocampus as well as enhanced hippocampal-dependent learning and memory. Recent evidence extends this relationship to elderly humans by suggesting that high aerobic fitness levels in older adults are associated with increased hippocampal volume and superior memory performance. The present study aimed to further extend the link between fitness, hippocampal volume, and memory to a sample of preadolescent children. To this end, magnetic resonance imaging was employed to investigate whether high- and low-fit 9- and 10-year-old children showed differences in hippocampal volume and if the differences were related to performance on an item and relational memory task. Relational but not item memory is primarily supported by the hippocampus. Consistent with predictions, high-fit children showed greater bilateral hippocampal volumes. Furthermore, hippocampal volume was positively associated with performance on the relational but not the item memory task. The findings are the first to suggest that aerobic fitness can impact the structure and function of the developing human brain.

Flexible regression models for functional neuroimaging

Current practice for analysing functional neuroimaging data is to average the brain signals recorded at multiple sensors or channels on the scalp over time across hundreds of trials or replicates to eliminate noise and enhance the underlying signal of interest. These studies recording brain signals non-invasively using functional neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) generate complex, high dimensional and noisy data for many subjects at a number of replicates. Single replicate (or single trial) analysis of neuroimaging data have gained focus as they are advantageous to study the features of the signals at each replicate without averaging out important features in the data that the current methods employ. The research here is conducted to systematically develop flexible regression mixed models for single trial analysis of specific brain activities using examples from EEG and MEG to illustrate the models. This thesis follows three specific themes: i) artefact correction to estimate the `brain' signal which is of interest, ii) characterisation of the signals to reduce their dimensions, and iii) model fitting for single trials after accounting for variations between subjects and within subjects (between replicates). The models are developed to establish evidence of two specific neurological phenomena - entrainment of brain signals to an $\alpha$ band of frequencies (8-12Hz) and dipolar brain activation in the same $\alpha$ frequency band in an EEG experiment and a MEG study, respectively.

Functional Neuroimaging of the Social Regulation of Emotion in Schizophrenia

Negative symptoms in schizophrenia are characterized by deficits in normative experiences and expression of emotion. Social anhedonia (diminished pleasure from social experiences) is one negative symptom that may impact patients’ motivation to engage in meaningful social relationships. Past research has begun to examine the mechanisms that underlie social anhedonia, but it is unclear how this lack of social interest may impact the typically positive effects of social buffering and social baseline theory whereby social support attenuates stress. The present pilot study examines how social affiliation through hand holding is related to subjective and neural threat processing, negative symptoms, and social functioning. Twenty-one participants (14 controls; 7 schizophrenia) developed social affiliation with a member of the research staff who served as the supportive partner during the threat task. Participants displayed greater subjective benefit to holding the hand of their partner during times of stress relative to being alone or with an anonymous experimenter, as indicated by self-reported increased positive valence and decreased arousal ratings. When examining the effects of group, hand holding, and their interaction on the neurological experience of threat during the fMRI task, the results were not significant. However, exploratory analyses identified preliminary data suggesting that controls experienced small relative increases in BOLD signal to threat when alone compared to being with the anonymous experimenter or their partner, whereas the schizophrenia group results indicated subtle relative decreases in BOLD signal to threat when alone compared to either of the hand holding conditions. Additionally, within the schizophrenia group, more positive valence in the partner condition was associated with less severe negative symptoms, better social functioning, and more social affiliation, whereas less arousal was correlated with more social affiliation. Our pilot study offers initial insights about the difficulties of building and using social affiliation and support through hand holding with individuals with schizophrenia during times of stress. Further research is necessary to clarify which types of support may be more or less beneficial to individuals with schizophrenia who may experience social anhedonia or paranoia with others that may challenge the otherwise positive effects of social buffering and maintaining a social baseline.

Behavioral and neuroimaging research of reading: A case of Japanese

Behavioral studies showed that AS, an English-Japanese bilingual was a skilled reader in Japanese but was a phonological dyslexic in English. This behavioral dissociation was accounted for by the Hypothesis of Transparency and Granularity postulated by Wydell & Butterworth. However, a neuroimaging study using MEG (magnetoencephalography) revealed that AS has the same functional deficit in the left superior temporal gyrus (STG). This paper therefore offers an answer to this intriguing discrepancy between the behavioral dissociation and the neural unity in AS by reviewing existing behavioral and neuroimaging studies in alphabetic languages such as English, Finnish, French, and Italian, and nonalphabetic languages such as Japanese and Chinese.

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.

Advanced neuroimaging methodologies to improve connectivity detection in normal and abnormal language brain networks

The language connectome was in-vivo investigated using multimodal non-invasive quantitative MRI. In PPA patients (n=18) recruited by the IRCCS ISNB, Bologna, cortical thickness measures showed a predominant reduction on the left hemisphere (p<0.005) with respect to matched healthy controls (HC) (n=18), and an accuracy of 86.1% in discrimination from Alzheimer’s disease patients (n=18). The left temporal and para-hippocampal gyri significantly correlated (p<0.01) with language fluency. In PPA patients (n=31) recruited by the Northwestern University Chicago, DTI measures were longitudinally evaluated (2-years follow-up) under the supervision of Prof. M. Catani, King’s College London. Significant differences with matched HC (n=27) were found, tract-localized at baseline and widespread in the follow-up. Language assessment scores correlated with arcuate (AF) and uncinate (UF) fasciculi DTI measures. In left-ischemic stroke patients (n=16) recruited by the NatBrainLab, King’s College London, language recovery was longitudinally evaluated (6-months follow-up). Using arterial spin labelling imaging a significant correlation (p<0.01) between language recovery and cerebral blood flow asymmetry, was found in the middle cerebral artery perfusion, towards the right. In HC (n=29) recruited by the DIBINEM Functional MR Unit, University of Bologna, an along-tract algorithm was developed suitable for different tractography methods, using the Laplacian operator. A higher left superior temporal gyrus and precentral operculum AF connectivity was found (Talozzi L et al., 2018), and lateralized UF projections towards the left dorsal orbital cortex. In HC (n=50) recruited in the Human Connectome Project, a new tractography-driven approach was developed for left association fibres, using a principal component analysis. The first component discriminated cortical areas typically connected by the AF, suggesting a good discrimination of cortical areas sharing a similar connectivity pattern. The evaluation of morphological, microstructural and metabolic measures could be used as in-vivo biomarkers to monitor language impairment related to neurodegeneration or as surrogate of cognitive rehabilitation/interventional treatment efficacy.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Infratentorial Gray Matter Atrophy And Excess In Primary Craniocervical Dystonia.

Primary craniocervical dystonia (CCD) is generally attributed to functional abnormalities in the cortico-striato-pallido-thalamocortical loops, but cerebellar pathways have also been implicated in neuroimaging studies. Hence, our purpose was to perform a volumetric evaluation of the infratentorial structures in CCD. We compared 35 DYT1/DYT6 negative patients with CCD and 35 healthy controls. Cerebellar volume was evaluated using manual volumetry (DISPLAY software) and infratentorial volume by voxel based morphometry of gray matter (GM) segments derived from T1 weighted 3 T MRI using the SUIT tool (SPM8/Dartel). We used t-tests to compare infratentorial volumes between groups. Cerebellar volume was (1.14 ± 0.17) × 10(2) cm(3) for controls and (1.13 ± 0.14) × 10(2) cm(3) for patients; p = 0.74. VBM demonstrated GM increase in the left I-IV cerebellar lobules and GM decrease in the left lobules VI and Crus I and in the right lobules VI, Crus I and VIIIb. In a secondary analysis, VBM demonstrated GM increase also in the brainstem, mostly in the pons. While gray matter increase is observed in the anterior lobe of the cerebellum and in the brainstem, the atrophy is concentrated in the posterior lobe of the cerebellum, demonstrating a differential pattern of infratentorial involvement in CCD. This study shows subtle structural abnormalities of the cerebellum and brainstem in primary CCD.

Multimodal Mri-based Study In Patients With Spg4 Mutations.

Mutations in the SPG4 gene (SPG4-HSP) are the most frequent cause of hereditary spastic paraplegia, but the extent of the neurodegeneration related to the disease is not yet known. Therefore, our objective is to identify regions of the central nervous system damaged in patients with SPG4-HSP using a multi-modal neuroimaging approach. In addition, we aimed to identify possible clinical correlates of such damage. Eleven patients (mean age 46.0 ± 15.0 years, 8 men) with molecular confirmation of hereditary spastic paraplegia, and 23 matched healthy controls (mean age 51.4 ± 14.1years, 17 men) underwent MRI scans in a 3T scanner. We used 3D T1 images to perform volumetric measurements of the brain and spinal cord. We then performed tract-based spatial statistics and tractography analyses of diffusion tensor images to assess microstructural integrity of white matter tracts. Disease severity was quantified with the Spastic Paraplegia Rating Scale. Correlations were then carried out between MRI metrics and clinical data. Volumetric analyses did not identify macroscopic abnormalities in the brain of hereditary spastic paraplegia patients. In contrast, we found extensive fractional anisotropy reduction in the corticospinal tracts, cingulate gyri and splenium of the corpus callosum. Spinal cord morphometry identified atrophy without flattening in the group of patients with hereditary spastic paraplegia. Fractional anisotropy of the corpus callosum and pyramidal tracts did correlate with disease severity. Hereditary spastic paraplegia is characterized by relative sparing of the cortical mantle and remarkable damage to the distal portions of the corticospinal tracts, extending into the spinal cord.

Relação dose-dependente do uso crônico de fenitoína e atrofia cerebelar em pacientes com epilepsia

The chronic treatment with phenytoin or the acute intoxication by this drug may cause permanent cerebellar injury with atrophy of cerebellum vermis and hemispheres, which can be detected by neuroimaging studies. The aim of the present study was to investigate the correlation between the dosage and duration of treatment with phenytoin and the occurrence of cerebellar atrophy. Sixty-six patients were studied and had their tomographies analyzed for cerebellar atrophy. Of the 66 patients studied, 18 had moderate/severe atrophy, 15 had mild atrophy and 33 were considered to be normal. The patients with moderate/severe atrophy were those with higher exposure to phenytoin (longer duration of treatment and higher total dosage) showing statistically significant difference when compared to patients with mild atrophy or without atrophy (p=0.02). Further, the patients with moderate/severe atrophy had serum levels of phenytoin statistically higher than those of patients with mild atrophy or without atrophy (p = 0.008). There was no association between other antiepileptic drugs dosage or duration of treatment and degree of cerebellar atrophy. We also found that older patients had cerebellar atrophy more frequently, indicating that age or duration of the seizure disorder may also be important in the determination of cerebellar degeneration in these patients. We conclude that although there is a possibility that repeated seizures contribute to cerebellar damage, long term exposure to phenytoin, particularly in high doses and toxic serum levels, cause cerebellar atrophy.

Síndrome do X Frágil com variante de Dandy-Walker: estudo clínico das manifestações comunicativas orais e escritas

A síndrome do X Frágil é a causa mais frequente de deficiência intelectual hereditária. A variante de Dandy-Walker trata-se de uma constelação específica de achados neurorradiológicos. Este estudo relata achados da comunicação oral e escrita de um menino de 15 anos com diagnóstico clínico e molecular da síndrome do X-Frágil e achados de neuroimagem do encéfalo compatíveis com variante de Dandy-Walker. A avaliação fonoaudiológica foi realizada por meio da Observação do Comportamento Comunicativo, aplicação do ABFW - Teste de Linguagem Infantil - Fonologia, Perfil de Habilidades Fonológicas, Teste de Desempenho Escolar, Teste Illinois de Habilidades Psicolinguísticas, avaliação do sistema estomatognático e avaliação audiológica. Observou-se: alteração de linguagem oral quanto às habilidades fonológicas, semânticas, pragmáticas e morfossintáticas; déficits nas habilidades psicolinguísticas (recepção auditiva, expressão verbal, combinação de sons, memória sequencial auditiva e visual, closura auditiva, associação auditiva e visual); e alterações morfológicas e funcionais do sistema estomatognático. Na leitura verificou-se dificuldades na decodificação dos símbolos gráficos e na escrita havia omissões, aglutinações e representações múltiplas com o uso predominante de vogais e dificuldades na organização viso-espacial. Em matemática, apesar do reconhecimento numérico, não realizou operações aritméticas. Não foram observadas alterações na avaliação audiológica periférica. A constelação de sintomas comportamentais, cognitivos, linguísticos e perceptivos, previstos na síndrome do X-Frágil, somada às alterações estruturais do sistema nervoso central, pertencentes à variante de Dandy-Walker, trouxeram interferências marcantes no desenvolvimento das habilidades comunicativas, no aprendizado da leitura e escrita e na integração social do indivíduo.

Cognitive control associated with irritability induction: an autobiographical recall fMRI study

OBJECTIVE: Despite the relevance of irritability emotions to the treatment, prognosis and classification of psychiatric disorders, the neurobiological basis of this emotional state has been rarely investigated to date. We assessed the brain circuitry underlying personal script-driven irritability in healthy subjects (n = 11) using functional magnetic resonance imaging. METHOD: Blood oxygen level-dependent signal changes were recorded during auditory presentation of personal scripts of irritability in contrast to scripts of happiness or neutral emotional content. Self-rated emotional measurements and skin conductance recordings were also obtained. Images were acquired using a 1,5T magnetic resonance scanner. Brain activation maps were constructed from individual images, and between-condition differences in the mean power of experimental response were identified by using cluster-wise nonparametric tests. RESULTS: Compared to neutral scripts, increased blood oxygen level-dependent signal during irritability scripts was detected in the left subgenual anterior cingulate cortex, and in the left medial, anterolateral and posterolateral dorsal prefrontal cortex (cluster-wise p-value < 0.05). While the involvement of the subgenual cingulate and dorsal anterolateral prefrontal cortices was unique to the irritability state, increased blood oxygen level-dependent signal in dorsomedial and dorsal posterolateral prefrontal regions were also present during happiness induction. CONCLUSION: Irritability induction is associated with functional changes in a limited set of brain regions previously implicated in the mediation of emotional states. Changes in prefrontal and cingulate areas may be related to effortful cognitive control aspects that gain salience during the emergence of irritability.

The Brain as a Distributed Intelligent Processing System: An EEG Study

Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion: The present results support these claims and the neural efficiency hypothesis.

The cerebral sulci and gyri

The aim of this study was to describe in detail the microanatomy of the cerebral sulci and gyri, clarifying the nomenclature for microneurosurgical purposes. An extensive review of the literature regarding the historical, evolutionary, embryological, and anatomical aspects pertinent to human cerebral sulci and gyri was conducted, with a special focus on microneuroanatomy issues in the field of neurosurgery. An intimate knowledge of the cerebral sulci and gyri is needed to understand neuroimaging studies, as well as to plan and execute current microneurosurgical procedures. (DOI: 10.3171/2009.11.FOCUS09245)

Frequency Domain Connectivity Identification: An Application of Partial Directed Coherence in fMRI

Functional magnetic resonance imaging (fMRI) has become an important tool in Neuroscience due to its noninvasive and high spatial resolution properties compared to other methods like PET or EEG. Characterization of the neural connectivity has been the aim of several cognitive researches, as the interactions among cortical areas lie at the heart of many brain dysfunctions and mental disorders. Several methods like correlation analysis, structural equation modeling, and dynamic causal models have been proposed to quantify connectivity strength. An important concept related to connectivity modeling is Granger causality, which is one of the most popular definitions for the measure of directional dependence between time series. In this article, we propose the application of the partial directed coherence (PDC) for the connectivity analysis of multisubject fMRI data using multivariate bootstrap. PDC is a frequency domain counterpart of Granger causality and has become a very prominent tool in EEG studies. The achieved frequency decomposition of connectivity is useful in separating interactions from neural modules from those originating in scanner noise, breath, and heart beating. Real fMRI dataset of six subjects executing a language processing protocol was used for the analysis of connectivity. Hum Brain Mapp 30:452-461, 2009. (C) 2007 Wiley-Liss, Inc.