Pattern Of Brain Damage Research Articles

Stable multivariate lesion symptom mapping.

Multivariate lesion-symptom mapping (MLSM) considers lesion information across the entire brain to predict impairments. The strength of this approach is also its weakness-considering many brain features together synergistically can uncover complex brain-behavior relationships but exposes a high-dimensional feature space that a model is expected to learn. Successfully distinguishing between features in this landscape can be difficult for models, particularly in the presence of irrelevant or redundant features. Here, we propose stable multivariate lesion-symptom mapping (sMLSM), which integrates the identification of reliable features with stability selection into conventional MLSM and describe our open-source MATLAB implementation. Usage is showcased with our publicly available dataset of chronic stroke survivors (N=167) and further validated in our independent public acute stroke dataset (N = 1106). We demonstrate that sMLSM eliminates inconsistent features highlighted by MLSM, reduces variation in feature weights, enables the model to learn more complex patterns of brain damage, and improves model accuracy for predicting aphasia severity in a way that tends to be robust regarding the choice of parameters for identifying reliable features. Critically, sMLSM more consistently outperforms predictions based on lesion size alone. This advantage is evident starting at modest sample sizes (N>75). Spatial distribution of feature importance is different in sMLSM, which highlights the features identified by univariate lesion symptom mapping while also implicating select regions emphasized by MLSM. Beyond improved prediction accuracy, sMLSM can offer deeper insight into reliable biomarkers of impairment, informing our understanding of neurobiology.

Which Convolutional Neural Network Architecture is the Best at Detecting Alzheimer’s Disease: A head‐to‐head Comparison Using Relevance Maps

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder which causes gradual and irreversible damage to the brain. These patterns of brain damage can be detected using T1‐weighted MRI scans by modern neuroimaging methods. Recent developments in convolutional neural networks (CNN) have achieved promising results in various computer‐vision tasks. Feature attribution methods such as layer‐wise relevance propagation (LRP) allow tracing back the information flow in CNNs to derive relevance heatmaps, which approximate the contribution of the input image regions on the model decision. We addressed the open question, which of the most common CNN architectures is best suited for medical image detection, i.e. AD classification based on MRI data.MethodFour CNN architectures are widely used in the literature: AlexNet, VGG, ResNet, and DenseNet. We adapted these CNN architectures to be used with 3D brain MRI data and trained the models on a heterogeneous dataset with N>2200 from four large studies. We applied tenfold cross‐validation and additionally evaluated the results on an independent test dataset.ResultThe more complex CNN architectures DenseNet and ResNet with ‘skip connections’, provided the best results for both group separation tasks of ‐ AD vs Cognitively Normal (CN), and Mild Cognitive Impairment (MCI) vs CN, although the overall differences in accuracy did not reach statistical significance. From the mean relevance maps obtained using the LRP relevance propagation method (see Fig. 1.), we found that DenseNet focused primarily on the medial temporal lobe and posterior cingulate cortex atrophy. ResNet’s mean relevance maps were more noisy and heterogeneous.ConclusionDenseNet provided the most focused relevance maps, best matching apriori expectations of brain regions contributing to the detection of AD. Its ‘dense connections’ between the layers enabled a highly efficient information flow at various scales. Our evaluation setup also demonstrated the value of a holistic evaluation of models, where relevance maps are used in combination with classical performance metrics.

Which Convolutional Neural Network Architecture is the Best at Detecting Alzheimer’s Disease: A head‐to‐head Comparison Using Relevance Maps

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder which causes gradual and irreversible damage to the brain. These patterns of brain damage can be detected using T1‐weighted MRI scans by modern neuroimaging methods. Recent developments in convolutional neural networks (CNN) have achieved promising results in various computer‐vision tasks. Feature attribution methods such as layer‐wise relevance propagation (LRP) allow tracing back the information flow in CNNs to derive relevance heatmaps, which approximate the contribution of the input image regions on the model decision. We addressed the open question, which of the most common CNN architectures is best suited for medical image detection, i.e. AD classification based on MRI data.MethodFour CNN architectures are widely used in the literature: AlexNet, VGG, ResNet, and DenseNet. We adapted these CNN architectures to be used with 3D brain MRI data and trained the models on a heterogeneous dataset with N>2200 from four large studies. We applied tenfold cross‐validation and additionally evaluated the results on an independent test dataset.ResultThe more complex CNN architectures DenseNet and ResNet with ‘skip connections’, provided the best results for both group separation tasks of ‐ AD vs Cognitively Normal (CN), and Mild Cognitive Impairment (MCI) vs CN, although the overall differences in accuracy did not reach statistical significance. From the mean relevance maps obtained using the LRP relevance propagation method (see Fig. 1.), we found that DenseNet focused primarily on the medial temporal lobe and posterior cingulate cortex atrophy. ResNet’s mean relevance maps were more noisy and heterogeneous.ConclusionDenseNet provided the most focused relevance maps, best matching apriori expectations of brain regions contributing to the detection of AD. Its ‘dense connections’ between the layers enabled a highly efficient information flow at various scales. Our evaluation setup also demonstrated the value of a holistic evaluation of models, where relevance maps are used in combination with classical performance metrics.

Study on brain damage patterns of COVID-19 patients based on EEG signals

ObjectiveThe coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by the SARA-CoV-2, characterized by high infectivity and incidence. Clinical data indicates that COVID-19 significantly damages patients’ perception, motor function, and cognitive function. However, the electrophysiological mechanism by which the disease affects the patient’s nervous system is not yet clear. Our aim is to investigate the abnormal levels of brain activity and changes in brain functional connectivity network in patients with COVID-19.MethodsWe compared and analyzed electroencephalography signal sample entropy, energy spectrum, and brain network characteristic parameters in the delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands of 15 patients with COVID-19 and 15 healthy controls at rest.ResultsAt rest, energy values of the four frequency bands in the frontal and temporal lobes of COVID-19 patients were significantly reduced. At the same time, the sample entropy value of the delta band in COVID-19 patients was significantly increased, while the value of the beta band was significantly decreased. However, the average value of the directed transfer function of patients did not show any abnormalities under the four frequency bands. Furthermore, node degree in the temporal lobe of patients was significantly increased, while the input degree of the frontal and temporal lobes was significantly decreased, and the output degree of the frontal and occipital lobes was significantly increased.ConclusionThe level of brain activity in COVID-19 patients at rest is reduced, and the brain functional network undergoes a rearrangement. These results preliminarily demonstrate that COVID-19 patients exhibit certain brain abnormalities during rest, it is feasible to explore the neurophysiological mechanism of COVID-19’s impact on the nervous system by using EEG signals, which can provide a certain technical basis for the subsequent diagnosis and evaluation of COVID-19 using artificial intelligence and the prevention of brain nervous system diseases after COVID-19 infection.

Using predictive validity to compare associations between brain damage and behavior.

Lesion-behavior mapping (LBM) provides a statistical map of the association between voxel-wise brain damage and individual differences in behavior. To understand whether two behaviors are mediated by damage to distinct regions, researchers often compare LBM weight outputs by either the Overlap method or the Correlation method. However, these methods lack statistical criteria to determine whether two LBM are distinct versus the same and are disconnected from a major goal of LBMs: predicting behavior from brain damage. Without such criteria, researchers may draw conclusions from numeric differences between LBMs that are irrelevant to predicting behavior. We developed and validated a predictive validity comparison method (PVC) that establishes a statistical criterion for comparing two LBMs using predictive accuracy: two LBMs are distinct if and only if they provide unique predictive power for the behaviors being assessed. We applied PVC to two lesion-behavior stroke data sets, demonstrating its utility for determining when behaviors arise from the same versus different lesion patterns. Using region-of-interest-based simulations derived from proportion damage from a large data set (n = 131), PVC accurately detected when behaviors were mediated by different regions (high sensitivity) versus the same region (high specificity). Both the Overlap method and Correlation method performed poorly on the simulated data. By objectively determining whether two behavioral deficits can be explained by single versus distinct patterns of brain damage, PVC provides a critical advance in establishing the brain bases of behavior. We have developed and released a GUI-driven web app to encourage widespread adoption.

Chapter 12 - Neuroimaging in mitochondrial disease

The anatomic complexity of the brain in combination with its high energy demands makes this organ specifically vulnerable to defects of mitochondrial oxidative phosphorylation. Therefore, neurodegeneration is a hallmark of mitochondrial diseases. The nervous system of affected individuals typically shows selective regional vulnerability leading to distinct patterns of tissue damage. A classic example is Leigh syndrome, which causes symmetric alterations of basal ganglia and brain stem. Leigh syndrome can be caused by different genetic defects (>75 known disease genes) with variable disease onset ranging from infancy to adulthood. Other mitochondrial diseases are characterized by focal brain lesions, which is a core feature of MELAS syndrome (mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes). Apart from gray matter, also white matter can be affected by mitochondrial dysfunction. White matter lesions vary depending on the underlying genetic defect and may progress into cystic cavities. In view of the recognizable patterns of brain damage in mitochondrial diseases, neuroimaging techniques play a key role in diagnostic work-up. In the clinical setting, magnetic resonance imaging (MRI) and MR spectroscopy (MRS) are the mainstay of diagnostic work-up. Apart from visualization of brain anatomy, MRS allows the detection of metabolites such as lactate, which is of specific interest in the context of mitochondrial dysfunction. However, it is important to note that findings like symmetric basal ganglia lesions on MRI or a lactate peak on MRS are not specific, and that there is a broad range of disorders that can mimic mitochondrial diseases on neuroimaging. In this chapter, we will review the spectrum of neuroimaging findings in mitochondrial diseases and discuss important differential diagnoses. Moreover, we will give an outlook on novel biomedical imaging tools that may provide interesting insights into mitochondrial disease pathophysiology.

Distinct clusters of neuropsychiatric symptoms associated with distinct patterns of brain damage

Distinct clusters of neuropsychiatric symptoms associated with distinct patterns of brain damage

Recovery from aphasia in the first year after stroke.

Most individuals who experience aphasia after a stroke recover to some extent, with the majority of gains taking place in the first year. The nature and time course of this recovery process is only partially understood, especially its dependence on lesion location and extent, which are the most important determinants of outcome. The aim of this study was to provide a comprehensive description of patterns of recovery from aphasia in the first year after stroke. We recruited 334 patients with acute left hemisphere supratentorial ischaemic or haemorrhagic stroke and evaluated their speech and language function within 5 days using the Quick Aphasia Battery (QAB). At this initial time point, 218 patients presented with aphasia. Individuals with aphasia were followed longitudinally, with follow-up evaluations of speech and language at 1 month, 3 months, and 1 year post-stroke, wherever possible. Lesions were manually delineated based on acute clinical MRI or CT imaging. Patients with and without aphasia were divided into 13 groups of individuals with similar, commonly occurring patterns of brain damage. Trajectories of recovery were then investigated as a function of group (i.e. lesion location and extent) and speech/language domain (overall language function, word comprehension, sentence comprehension, word finding, grammatical construction, phonological encoding, speech motor programming, speech motor execution, and reading). We found that aphasia is dynamic, multidimensional, and gradated, with little explanatory role for aphasia subtypes or binary concepts such as fluency. Patients with circumscribed frontal lesions recovered well, consistent with some previous observations. More surprisingly, most patients with larger frontal lesions extending into the parietal or temporal lobes also recovered well, as did patients with relatively circumscribed temporal, temporoparietal, or parietal lesions. Persistent moderate or severe deficits were common only in patients with extensive damage throughout the middle cerebral artery distribution or extensive temporoparietal damage. There were striking differences between speech/language domains in their rates of recovery and relationships to overall language function, suggesting that specific domains differ in the extent to which they are redundantly represented throughout the language network, as opposed to depending on specialized cortical substrates. Our findings have an immediate clinical application in that they will enable clinicians to estimate the likely course of recovery for individual patients, as well as the uncertainty of these predictions, based on acutely observable neurological factors.

Cortical thickness in clinical moyamoya disease: A magnetic resonance imaging study.

Moyamoya disease (MMD) is a progressive cerebrovascular disorder, with an unknown pathogenesis and aetiology. MMD is characterized by steno-occlusive changes at the terminal portion of the internal carotid artery (ICA), which is accompanied by variable development of the basal collaterals, also known as moyamoya vessels. Patients with MMD show variable patterns of brain damage and may experience recurrent multiple transient ischaemic attacks, intracranial bleeding and cerebral infarction. In this study, we investigate the potential for structural T1 magnetic resonance imaging (MRI) to help characterize abnormal cortical development in MMD clinically, with an analysis of both average and variability of regional cortical thicknesses. This study also included a machine learning analysis to assess the predictive capacity of the cortical thickness abnormalities observed in this research. This study included 993 MRI examinations from neurotypical controls and 269 MRI examinations from MMD patients. Results demonstrate abnormal cortical presentation of the insula, caudate, postcentral, precuneus and cingulate regions, in agreement with previous literature cortical thickness findings as well as alternative methods such as functional MRI (fMRI) and digital angiography. To the best of our knowledge, this is the first manuscript to report cortical thickness abnormalities in the middle temporal visual area in MMD and the first study to report on cortical thickness variability abnormalities in MMD.

The neural bases of discourse semantic and pragmatic deficits in patients with frontotemporal dementia and Alzheimer's disease

Neuropsychological research on language has largely focused on how the brain processes single words and sentences whose meaning does not depend on the context or on the intentions of the speaker. Fewer studies have investigated the neurobiological bases of discourse semantics and pragmatics in patients and healthy individuals. We studied discourse semantic and pragmatic skills in patients with behavioral variant frontotemporal dementia (bvFTD) or Alzheimer's disease (AD) in comparison to healthy controls. Our goal was to assess whether and how the two patient groups differ in their cognitive and behavioral profiles, and whether these differences may be traced back to disease-specific patterns of neuronal hypometabolism. We combined PET imaging with standard neuropsychological assessment tools and a dedicated test battery designed to evaluate discourse semantics and pragmatics in patients with brain lesions or neurological disorders. We found that AD and bvFTD patients were both impaired compared to controls in discourse comprehension, but largely spared in single word comprehension. Importantly, we also found evidence for behavioral impairments specific to each disease, associated with different brain damage patterns. Compared to AD and controls, bvFTD patients had, behaviorally, more difficulty in evaluating whether certain inferences follow from discourse and in identifying humorous completions of stories; neurally, they had greater damage to medial and lateral regions of PFC. AD patients showed a different pattern of errors in a humor comprehension task than bvFTD patients and controls, and they showed greater posterior temporal and parietal cortical depletion. Both groups had comparable difficulties with understanding idioms and indirect requests. Finally, bvFTD-specific errors were correlated with the severity of hypometabolism in bvFTD. We discuss these results in light of previous research on the dementias as well as consequences for models of semantics and pragmatics in the brain.

Pathoanatomical Pattern of Brain Damage of White Mice Infected with Experimental Anthrax

Background. Bacillus anthracis is a microorganism that causes anthrax. Because of irrational therapy, this particularly dangerous infectious disease leads to a systemic spread of bacteria in the body through histohematological barriers. Bacilli entering the brain subsequently lead to hemorrhagic meningitis. Despite intensive antibiotic therapy, that kind of meningitis is difficult to cure and therefore highly lethal. Studying characteristics of anthrax’s isolates of different origin and genotype is an actual area of research.The aim of the study is searching for pathomorphological and histological changes in the brain regions of experimental animals with anthrax infection, caused by B. anthracis with different plasmid spectrum.Materials and methods: The study was conducted on 200 certified white mice, three B. anthracis strains were used as research objects: B. anthracis I-323 (рХО1– /рХО2– ), B. anthracis I-275 (рХО1- /рХО2- ), and B. anthracis I-217 (рХО1+ / рХО2– ). The material for histological examination was the brain of mice, embedded in paraffin, and then sections were prepared using a microtome and stained with hematoxylin-eosin and Nissl toluidine blue. The degree of neuronal damage was assessed by calculating the semi-quantitative factor and determining the average size of the neuron nuclei, and the numerical density of cells in 1mm2 was studied. Microphotography and quantitative analysis was performed using the Motic Images Plus 2.0 application package. Statistical processing of the results was performed using the program “Statistica 6.0”.The results of the study showed that the brain of infected mice shows signs of hemorrhagic leptomeningitis, the manifestations of which are more pronounced in mice infected with weakly virulent plasmid strains of B. anthracis and fallen mice.Conclusion. Multicomponent exotoxin and specialized proteins, encoded in the pathogenicity plasmid of microorganisms, allow B. anthracis to easily overcome histohematological barriers and cause severe septic anthrax. The results obtained during the study supplement the available information on the pathogenesis of anthrax and indicate the need for further research in this direction.

Dissociation between frontal and temporal-parietal contributions to connected speech in acute stroke.

Humans are uniquely able to retrieve and combine words into syntactic structure to produce connected speech. Previous identification of focal brain regions necessary for production focused primarily on associations with the content produced by speakers with chronic stroke, where function may have shifted to other regions after reorganization occurred. Here, we relate patterns of brain damage with deficits to the content and structure of spontaneous connected speech in 52 speakers during the acute stage of a left hemisphere stroke. Multivariate lesion behaviour mapping demonstrated that damage to temporal-parietal regions impacted the ability to retrieve words and produce them within increasingly complex combinations. Damage primarily to inferior frontal cortex affected the production of syntactically accurate structure. In contrast to previous work, functional-anatomical dissociations did not depend on lesion size likely because acute lesions were smaller than typically found in chronic stroke. These results are consistent with predictions from theoretical models based primarily on evidence from language comprehension and highlight the importance of investigating individual differences in brain-language relationships in speakers with acute stroke.

Fatal Penetrating Head Injuries Caused by Projectile Tear Gas Canisters

Since their advent in the 1920s, tear gas canisters (TGCs) have been frequently used in crowd control. Few reports have documented nonpenetrating injuries attributed directly to TGCs. In this study, we report a case series of fatal penetrating head injuries caused by TGCs. We conducted a retrospective chart review of all the patients who were admitted to the Neurosurgery Teaching Hospital in Baghdad, Iraq, since the start of the antigovernment protests (October 2019). All patients who suffered penetrating head trauma caused by TGCs were included in our study. We collected patient demographics, wound location, neurologic examination, computed tomography (CT) scan findings, surgical management, and clinical outcomes. We found 10 cases of penetrating head trauma caused by TGCs. All victims were men, with a mean age of 16 years (range, 14-19 years). The mean Glasgow Coma Scale score was 7 (range, 3-10). The neurologic examination revealed unilateral hemiplegia/hemiparesis and pupillary abnormality in 40% (n= 4) and 50% (n= 5) of the patients, respectively. CT scans revealed an extensive pattern of brain damage. Surgical intervention was done in 80% of cases (n= 8), which included removal of the TGC, wound debridement, and hemostasis. The in-hospital mortality rate was 100% (N= 10), with all fatalities occurring within 1-3 days of admission. TGCs have the potential to cause lethal penetrating head injuries, calling for a reevaluation of their safety and methods of use in terms of human health.

Possibilities of Magnetic Resonance Imaging of the Brain in Infantile Cerebral Palsy

Infantile cerebral palsy (ICP) is the main cause of childhood disability and is characterized by a non-progressive lesion and/or impaired development of the brain in a foetus or newborn. Magnetic resonance imaging (MRI) is a modern non-invasive method with extensive capabilities for diagnosing brain damage in ICP. The review focuses on anatomical structural MR patterns of brain damage in ICP and gives the present-day classification of MR changes in this disease. The role of MRI in determining the duration of brain damage in ICP has been considered. Data on the ratio of ICP phenotypes to pathological MR findings has been presented. Neuroimaging prognostic biomarkers are discussed. It is emphasized that many questions regarding the prognostic significance of MR findings remain unresolved; prospects are associated with the use of new MRI modalities such as functional and diffusiontensor MRI.

Structural, Microstructural, and Metabolic Alterations in Primary Progressive Aphasia Variants.

Neuroimaging studies have described the brain alterations in primary progressive aphasia (PPA) variants (semantic, logopenic, nonfluent/agrammatic). However, few studies combined T1, FDG-PET, and diffusion MRI techniques to study atrophy, hypometabolism, and tract alterations across the three PPA main variants. We therefore explored a large early-stage cohort of semantic, logopenic and nonfluent/agrammatic variants (N = 86) and of 23 matched healthy controls with anatomical MRI (cortical thickness), FDG PET (metabolism) and diffusion MRI (white matter tracts analyses), aiming at identifying cortical and sub-cortical brain alterations, and confronting these alterations across imaging modalities and aphasia variants. In the semantic variant, there was cortical thinning and hypometabolism in anterior temporal cortices, with left-hemisphere predominance, extending toward posterior temporal regions, and affecting tracts projecting to the anterior temporal lobes (inferior longitudinal fasciculus, uncinate fasciculus) and tracts projecting to or running nearby posterior temporal cortices: (superior longitudinal fasciculus, inferior frontal-occipital fasciculus). In the logopenic variant metabolic alterations were more extensive than atrophy affecting mainly the left temporal-parietal junction and extending toward more anterior temporal cortices. Metabolic and tract data were coherent given the alterations of the left superior and inferior longitudinal fasciculus and the left inferior frontal-occipital fasciculus. In the nonfluent/agrammatic variant cortical thinning and hypometabolism were located in the left frontal cortex but Broca's area was only affected on metabolic measures. Metabolic and tract alterations were coherent as reflected by damage to the left uncinate fasciculus connecting with Broca's area. Our findings provide a full-blown statistically robust picture of brain alterations in early-stage variants of primary progressive aphasia which has implications for diagnosis, classification and future therapeutic strategies. They demonstrate that in logopenic and semantic variants patterns of brain damage display a non-negligible overlap in temporal regions whereas they are substantially distinct in the nonfluent/agrammatic variant (frontal regions). These results also indicate that frontal networks (combinatorial syntax/phonology) and temporal networks (lexical/semantic representations) constitute distinct anatomo-functional entities with differential vulnerability to degenerative processes in aphasia variants. Finally, the identification of the specific damage patterns could open an avenue for trans-cranial stimulation approaches by indicating the appropriate target-entry into the damaged language system.

Neural circuits activated by error amplification and haptic guidance training techniques during performance of a timing-based motor task by healthy individuals

To promote motor learning, robotic devices have been used to improve subjects’ performance by guiding desired movements (haptic guidance—HG) or by artificially increasing movement errors to foster a more rapid learning (error amplification—EA). To better understand the neurophysiological basis of motor learning, a few studies have evaluated brain regions activated during EA/HG, but none has compared both approaches. The goal of this study was to investigate using fMRI which brain networks were activated during a single training session of HG/EA in healthy adults learning to play a computerized pinball-like timing task. Subjects had to trigger a robotic device by flexing their wrist at the correct timing to activate a virtual flipper and hit a falling ball towards randomly positioned targets. During training with HG/EA, subjects’ timing errors were decreased/increased, respectively, by the robotic device to delay or accelerate their wrist movement. The results showed that at the beginning of the training period with HG/EA, an error-detection network, including cerebellum and angular gyrus, was activated, consistent with subjects recognizing discrepancies between their intended actions and the actual movement timing. At the end of the training period, an error-detection network was still present for EA, while a memory consolidation/automatization network (caudate head and parahippocampal gyrus) was activated for HG. The results indicate that training movement with various kinds of robotic input relies on different brain networks. Better understanding the neurophysiological underpinnings of brain processes during HG/EA could prove useful for optimizing rehabilitative movement training for people with different patterns of brain damage.

Tracking brain damage in progressive supranuclear palsy: a longitudinal MRI study

ObjectivesIn this prospective, longitudinal, multiparametric MRI study, we investigated clinical as well as brain grey matter and white matter (WM) regional changes in patients with progressive supranuclear palsy-Richardson’s syndrome (PSP-RS).MethodsTwenty-one...

The role of left insula in executive set-switching: Lesion evidence from an acute stroke cohort

Impairments in executive functions are common in stroke survivors, both in the acute and in the chronic phase. However, little is known about the underlying lesion neuroanatomy of these deficits. This study aimed to elucidate the pattern of brain damage underlying executive dysfunction in a large and acute stroke cohort. Executive set-switching deficits were evaluated by a shape-based analogue of the Trail Making Test (from the Oxford Cognitive Screen) in a consecutive sample of 144 stroke patients (age: 70 ± 15 years, examination: 5 ± 4 days post-stroke; brain imaging: 1.7 ± 2.9 days post-stroke). A voxelwise lesion-symptom mapping analysis was performed by combining executive set-switching accuracy scores with manually delineated lesions on computerized tomography or magnetic resonance imaging scans. The analysis showed that lesions within the left insular cortex and adjacent white matter predicted poorer executive set-switching. Further analyses confirmed that the lesion effect in the left insula survived correction for the low-level visuospatial and motor component processes of executive set-switching. In conclusion, the study provides lesion-based evidence for the role of the left insular cortex in flexible switching of attention. The findings are consistent with emergent models of insular function postulating the role of this region in regulatory aspects of goal-directed behaviour.

Gender Peculiarities of Postresuscitation in the Expression of Brain-Derived Neurotrophic Factor (BDNF)

Purpose : to identify gender peculiarities of postresuscitation shifts in BDNF expression and neuronal death. Materials and Methods . At different points of the postresuscitation period (days 1-, 4-, 7-, and 14), the condition of highly sensitive to hypoxia neuronal populations (pyramidal neurons of hippocampus and Purkinje cells of cerebellum) were studied in white mature female rats exposed to a 10-minute stop of systemic blood circulation (compression of vascular fascicle of the heart). Sham operated animals were used as the control. Immunohistochemical detection of BDNF-immunoreactive neurons followed with determination of optical density, number of cells with different levels of BDNF expression, and total count of neurons per 1 mm of the length of their layer was carried out. The work was done using the image analysis system (computer Intel, microscope Olympus BX-41, software ImadgeScopeM, ImageJ 1,48v, Excel 2007). Statistic processing of data was performed with the aid of Statistica 7.0 using Kolmogorov-Smirnov λ test, Mann-Whitney U test, and Student's t-test. Results . The dynamics of postresuscitation shifts in BDNF-immunoreactivity of neuronal populations highly sensitive to hypoxia was studied in rats. Purkinje cells population in tissue slides from brain specimens harvested from female rats the alterations in BDNF expression became evident. This pattern was accompanied by the death of neurons. Those shifts in female animals were found to develop later than in male rats — by day 7 of the postresuscitation. Only BDNF-negative and BDNF-weakly positive neurons not survived postresuscitation. In the population of pyramidal cells of hippocampus in females, in contrast to males, there were no quantitative changes in BDNF molecules as revealed by immunohistochemistry and neuronal death process did not develop. Conclusion . Gender peculiarities in the development of postresuscitation shifts in BDNF expression and associated therewith death of neurons were revealed. It was shown that after cardiac arrest of the same duration, the postresuscitation shifts in BDNF expression and neuronal death manifested mostly in males compared to females. At the same time, animals of both genders demonstrate common postresuscitation brain alterations evidencing connection between the level of BDNF expression in neurons and their resistance to ischemia-reperfusion. Gender-specific patterns of brain damage and their importance for understanding the mechanisms of post-hypoxic encephalopathies are discussed.

Direct voxel-based comparisons between grey matter shrinkage and glucose hypometabolism in chronic alcoholism

Alcoholism is associated with widespread brain structural abnormalities affecting mainly the frontocerebellar and the Papez’s circuits. Brain glucose metabolism has received limited attention, and few studies used regions of interest approach and showed reduced global brain metabolism predominantly in the frontal and parietal lobes. Even though these studies have examined the relationship between grey matter shrinkage and hypometabolism, none has performed a direct voxel-by-voxel comparison between the degrees of structural and metabolic abnormalities. Seventeen alcoholic patients and 16 control subjects underwent both structural magnetic resonance imaging and 18F-2-fluoro-deoxy-glucose-positron emission tomography examinations. Structural abnormalities and hypometabolism were examined in alcoholic patients compared with control subjects using two-sample t-tests. Then, these two patterns of brain damage were directly compared with a paired t-test. Compared to controls, alcoholic patients had grey matter shrinkage and hypometabolism in the fronto-cerebellar circuit and several nodes of Papez’s circuit. The direct comparison revealed greater shrinkage than hypometabolism in the cerebellum, cingulate cortex, thalamus and hippocampus and parahippocampal gyrus. Conversely, hypometabolism was more severe than shrinkage in the dorsolateral, premotor and parietal cortices. The distinct profiles of abnormalities found within the Papez’s circuit, the fronto-cerebellar circuit and the parietal gyrus in chronic alcoholism suggest the involvement of different pathological mechanisms.