Structural Changes in the Brain in Depression and Relationship to Symptom Recurrence

CNS Spectrums ◽  
2002 ◽  
Vol 7 (2) ◽  
pp. 129-139 ◽  
Author(s):  
J. Douglas Bremner
Keyword(s):  
Frontal Cortex ◽  
Brain Structure ◽  
Structural Changes ◽  
Late Onset ◽  
Public Health Problem ◽  
White Matter Lesions ◽  
Brain Regions ◽  
Important Public Health Problem ◽  
Symptoms Of Depression ◽  
The Brain

ABSTRACTDepression is an important public health problem affecting about 15% of the general population; however, little is known about possible changes in the brain that might underlie the disorder. Neuroimaging has been a powerful tool to map actual changes in the brain structure of depressed patients that might be directly related to their symptoms of depression. Some imaging studies of brain structure have shown smaller hippocampal volume with the chronicity of depression correlating to a reduction in volume. Although the meaning of these findings is unclear, other studies have shown increased amygdala volume. Studies have found reductions in volume of the frontal cortex, with some studies showing specific reductions in subregions of the frontal cortex, including the orbitofrontal cortex. Findings of an increase in white matter lesions in elderly patients with depression have been replicated and correlated with late-onset depression, as well as impairments in social and cognitive function. These findings point to alterations in a circuit of brain regions hypothesized to include the frontal cortex, hippocampus, amygdala, striatum, and thalamus, that underlie symptoms of depression.

scholarly journals Musical skills can be decoded from magnetic resonance images

2020 ◽  
Author(s):  
Tuomas Puoliväli ◽  
Tuomo Sipola ◽  
Anja Thiede ◽  
Marina Kliuchko ◽  
Brigitte Bogert ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Structure ◽  
Structural Changes ◽  
Musical Instrument ◽  
Structural Features ◽  
Brain Regions ◽  
Magnetic Resonance Images ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Brain

AbstractLearning induces structural changes in the brain. Especially repeated, long-term behaviors, such as extensive training of playing a musical instrument, are likely to produce characteristic features to brain structure. However, it is not clear to what extent such structural features can be extracted from magnetic resonance images of the brain. Here we show that it is possible to predict whether a person is a musician or a non-musician based on the thickness of the cerebral cortex measured at 148 brain regions encompassing the whole cortex. Using a supervised machine learning technique called support vector machines, we achieved significant (κ = 0.321, p < 0.001) agreement between the actual and predicted participant groups of 30 musicians and 85 non-musicians. The areas contributing to the prediction were mostly in the frontal, parietal, and occipital lobes of the left hemisphere. Our results suggest that decoding an acquired skill from magnetic resonance images of brain structure is feasible to some extent. Further, the distribution of the areas that were informative in the classification, which mostly, but not entirely overlapped with earlier findings, implies that decoding-based analyses of structural properties of the brain can reveal novel aspects of musical aptitude.

Neural Mechanisms of Negative Symptoms

1989 ◽  
Vol 155 (S7) ◽  
pp. 93-98 ◽  
Author(s):  
Nancy C. Andreasen
Keyword(s):  
Frontal Cortex ◽  
Negative Symptoms ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Brain Dysfunction ◽  
Neural Mechanisms ◽  
Dementia Praecox ◽  
Intellectual Abilities ◽  
The Brain ◽  
Psychic Mechanisms

When Kraepelin originally defined and described dementia praecox, he assumed that it was due to some type of neural mechanism. He hypothesised that abnormalities could occur in a variety of brain regions, including the prefrontal, auditory, and language regions of the cortex. Many members of his department, including Alzheimer and Nissl, were actively involved in the search for the neuropathological lesions that would characterise schizophrenia. Although Kraepelin did not use the term ‘negative symptoms', he describes them comprehensively and states explicitly that he believes the symptoms of schizophrenia can be explained in terms of brain dysfunction:“If it should be confirmed that the disease attacks by preference the frontal areas of the brain, the central convolutions and central lobes, this distribution would in a certain measure agree with our present views about the site of the psychic mechanisms which are principally injured by the disease. On various grounds, it is easy to believe that the frontal cortex, which is specially well developed in man, stands in closer relation to his higher intellectual abilities, and these are the faculties which in our patients invariably suffer profound loss in contrast to memory and acquired ability.” Kraepelin (1919, p. 219)

the Comparred Effect of Zyprasidone and Haloperidol at Hippocampal and Frontal Cortex at Rats

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
D. Marinescu ◽  
L. Mogoanta ◽  
T. Udristoiu
Keyword(s):  
Adverse Event ◽  
Frontal Cortex ◽  
Negative Symptoms ◽  
Structural Changes ◽  
Saline Solution ◽  
Neuroprotective Effect ◽  
Neuronal Loss ◽  
D2 Receptors ◽  
Cerebral Structure ◽  
The Brain

Background:The alteration of hippocampal and prefrontal structures is linked with schizophrenia cognitive impairment and negative symptoms. the antipsychotics can induced apoptotic mechanisms correlated with the psychopharmacological mechanism of excesive blocking of the D2 receptors. Distress determined increase of the glucocorticoid aggression wich drive to the decrease of neuroprotective capacity at the brain level.Methods:We formed 5 study lots (5 adults rats) and a control lot. the substancies were administrated intraperitoneal, daily, saline solution equivalent to: ziprasidone (1.25mg/kg/day) and haloperidole (0.20mg/kg/day), dexametasone (0.20mg/kg/day):N1 - Haloperidole; N2 - Dexametasone; N3 - Ziprasidone; N4 - Dexametasone and Haloperidole; N5 - Dexametasone and Ziprasidone; N6 -control lot.We monitorised the cardiovascular function, respiration and EPS, without signaling any serious deadly adverse event. the rats were sacrificed during the 10th day and 21th day.Results:Frontal cortex and hippocamp were the most intensely affected even since the 10-th day to the N4 (haloperidole and dexametasone) lot with massive neuronal loss at the VI, V, and IV frontal cerebral layers.The lots treated with ziprasidone presented significant lesser structural changes in frontal cortex and hippocamp, comparative to haloperidole. the lots treated with dexametasone and ziprasidone (N5) are lesser affected at the cerebral structure level.Conclusions:Haloperidole has a significant decrease in neuroprotection. Ziprasidone demonstrated an neuroprotective effect.

scholarly journals Experience, but not age, is associated with volumetric mushroom body expansion in solitary alkali bees

2021 ◽  
pp. jeb.238899
Author(s):  
Mallory A. Hagadorn ◽  
Makenna M. Johnson ◽  
Adam R. Smith ◽  
Marc A. Seid ◽  
Karen M. Kapheim
Keyword(s):  
Structural Changes ◽  
Evolutionary Relationship ◽  
Brain Regions ◽  
Mushroom Bodies ◽  
Order Processing ◽  
Social Species ◽  
Evolution Of Sociality ◽  
Foraging Experience ◽  
Brain Changes ◽  
The Brain

In social insects, changes in behavior are often accompanied by structural changes in the brain. This neuroplasticity may come with experience (experience-dependent) or age (experience-expectant). Yet, the evolutionary relationship between neuroplasticity and sociality is unclear, because we know little about neuroplasticity in the solitary relatives of social species. We used confocal microscopy to measure brain changes in response to age and experience in a solitary halictid bee (Nomia melanderi). First, we compared the volume of individual brain regions among newly-emerged females, laboratory females deprived of reproductive and foraging experience, and free-flying, nesting females. Experience, but not age, led to significant expansion of the mushroom bodies—higher-order processing centers associated with learning and memory. Next, we investigated how social experience influences neuroplasticity by comparing the brains of females kept in the laboratory either alone or paired with another female. Paired females had significantly larger olfactory regions of the mushroom bodies. Together, these experimental results indicate that experience-dependent neuroplasticity is common to both solitary and social taxa, whereas experience-expectant neuroplasticity may be an adaptation to life in a social colony. Further, neuroplasticity in response to social chemical signals may have facilitated the evolution of sociality.

scholarly journals Neurotoxic astrocytic glypican-4 drives APOE4-dependent tau pathology

2021 ◽  
Author(s):  
Sivaprakasam Ramamoorthy ◽  
Kirill Gorbachev ◽  
Ana Pereira
Keyword(s):  
Late Onset ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Brain Regions ◽  
Tau Proteins ◽  
Tau Pathology ◽  
Density Lipoprotein Receptor ◽  
Apoe Receptor ◽  
The Brain

Apolipoprotein E4 (APOE4) is the crucial genetic risk factor of late-onset Alzheimer disease (AD). Aggregation of tau proteins into insoluble filaments and their spreading across the brain regions are major drivers of neurodegeneration in tauopathies, including in AD. However, the exact mechanisms through which APOE4 induces tau pathology remains unknown. Here, we report that the astrocyte-secreted protein glypican-4 (GPC-4), a novel binding partner of APOE4, drives tau pathology. GPC-4 preferentially interacts with APOE4 in comparison to other APOE isoforms and post-mortem APOE4-carrying AD brains highly express GPC-4 in neurotoxic astrocytes. The astrocyte-secreted GPC-4 induced both tau accumulation and propagation in vitro. CRISPR/dCas9 mediated activation of GPC-4 in a tauopathy animal model robustly induced tau pathology. Further, APOE4-induced tau pathology was greatly diminished in the absence of GPC-4. We found that GPC-4 promoted the stabilization of the APOE receptor low-density lipoprotein receptor-related protein 1 (LRP1) on the cellular surface, which effectively facilitates endocytosis of tau protein. Together, our data comprehensively demonstrate that one of the key APOE4-induced tau pathologies is directly mediated by GPC-4.

scholarly journals The Antennal Pathway of Dragonfly Nymphs, from Sensilla to the Brain

Insects ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 886
Author(s):  
Silvana Piersanti ◽  
Manuela Rebora ◽  
Gianandrea Salerno ◽  
Sylvia Anton
Keyword(s):  
Brain Structure ◽  
Antennal Lobe ◽  
Sensory Information ◽  
Brain Structures ◽  
Brain Regions ◽  
Adult Brain ◽  
Published Data ◽  
Antennal Sensilla ◽  
Aquatic Life ◽  
The Brain

Dragonflies are hemimetabolous insects, switching from an aquatic life style as nymphs to aerial life as adults, confronted to different environmental cues. How sensory structures on the antennae and the brain regions processing the incoming information are adapted to the reception of fundamentally different sensory cues has not been investigated in hemimetabolous insects. Here we describe the antennal sensilla, the general brain structure, and the antennal sensory pathways in the last six nymphal instars of Libellula depressa, in comparison with earlier published data from adults, using scanning electron microscopy, and antennal receptor neuron and antennal lobe output neuron mass-tracing with tetramethylrhodamin. Brain structure was visualized with an anti-synapsin antibody. Differently from adults, the nymphal antennal flagellum harbors many mechanoreceptive sensilla, one olfactory, and two thermo-hygroreceptive sensilla at all investigated instars. The nymphal brain is very similar to the adult brain throughout development, despite the considerable differences in antennal sensilla and habitat. Like in adults, nymphal brains contain mushroom bodies lacking calyces and small aglomerular antennal lobes. Antennal fibers innervate the antennal lobe similar to adult brains and the gnathal ganglion more prominently than in adults. Similar brain structures are thus used in L. depressa nymphs and adults to process diverging sensory information.

scholarly journals Relationship Between Type 2 Diabetes and White Matter Hyperintensity: A Systematic Review

2020 ◽  
Vol 11 ◽  
Author(s):  
Dan-Qiong Wang ◽  
Lei Wang ◽  
Miao-Miao Wei ◽  
Xiao-Shuang Xia ◽  
Xiao-Lin Tian ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
White Matter ◽  
Structural Changes ◽  
White Matter Lesions ◽  
Poor Performance ◽  
White Matter Hyperintensity ◽  
Diabetic Microangiopathy ◽  
The Relationship ◽  
The Brain

White matter (WM) disease is recognized as an important cause of cognitive decline and dementia. White matter lesions (WMLs) appear as white matter hyperintensities (WMH) on T2-weighted magnetic resonance imaging (MRI) scans of the brain. Previous studies have shown that type 2 diabetes (T2DM) is associated with WMH. In this review, we reviewed the literature on the relationship between T2DM and WMH in PubMed and Cochrane over the past five years and explored the possible links among the presence of T2DM, the course or complications of diabetes, and WMH. We found that: (1) Both from a macro- and micro-scopic point of view, most studies support the relationship of a larger WMH and a decrease in the integrity of WMH in T2DM; (2) From the relationship between brain structural changes and cognition in T2DM, the poor performance in memory, attention, and executive function tests associated with abnormal brain structure is consistent; (3) Diabetic microangiopathy or peripheral neuropathy may be associated with WMH, suggesting that the brain may be a target organ for T2DM microangiopathy; (4) Laboratory markers such as insulin resistance and fasting insulin levels were significantly associated with WMH. High HbA1c and high glucose variability were associated with WMH but not glycemic control.

scholarly journals Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation

2016 ◽  
Author(s):  
Elena Szefer ◽  
Donghuan Lu ◽  
Farouk Nathoo ◽  
Mirza Faisal Beg ◽  
Jinko Graham ◽  
...  
Keyword(s):  
Structural Changes ◽  
Late Onset ◽  
Brain Regions ◽  
Imaging Genetics ◽  
Tuning Parameter ◽  
Importance Measure ◽  
Biased Sampling ◽  
Alzheimers Disease ◽  
Validation Data ◽  
Linear Association

AbstractBoth genetic variants and brain region abnormalities are recognized to play a role in cognitive decline. We explore the association between singlenucleotide polymorphisms (SNPs) in linkage regions for Alzheimer’s disease and rates of decline in brain structure using data from the Alzheimers Disease Neuroimaging Initiative (ADNI).In an initial discovery stage, we assessed the presence of linear association between the minor allele counts of 75,845 SNPs in the Alzgene linkage regions and predicted rates of change in structural MRI measurements for 56 brain regions using an RV test. In a second, refinement stage, we reduced the number of SNPs using a bootstrap-enhanced sparse canonical correlation analysis (SCCA) with a fixed tuning parameter. Each SNP was assigned an importance measure proportional to the number of times it was estimated to have a nonzero coefficient in repeated re-sampling from the ADNI-1 sample. We created refined lists of SNPs based on importance probabilities greater than 50% and 90%, respectively. In a third, validation stage, we assessed the multivariate association between these refined lists of SNPs and the rates of structural change in the independent ADNI-2 study dataset.There was strong statistical evidence for linear association between the SNPs in the Alzgene linkage regions and the 56 imaging phenotypes in both the ADNI-1 and ADNI-2 samples (p < 0.0001). The bootstrap-enhanced SCCA identified 1,694 priority SNPs with importance probabilities > 50% and 22 SNPs with importance probabilities > 90%. The 1,694 prioritized SNPs in the ADNI-1 data were associated with imaging phenotypes in the ADNI-2 data (p = 0.0021).This manuscript presents an analysis that addresses challenges in current imaging genetics studies such as biased sampling designs and highdimensional data with low-signal. Genes corresponding to priority SNPs having the highest contribution in the validation data have previously been implicated or hypothesized to be implicated in AD, including GCLC, IDE, and STAMBP1andFAS. We hypothesize that the effect sizes of the 1,694 SNPs in the priority set are likely small, but further investigation within this set may advance understanding of the missing heritability in late-onset Alzheimers disease. Multivariate analysis; Linkage regions; Imaging genetics; Endophenotypes; Inverse probability weighting; Variable importance probabilities

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

Do antipsychotic drugs affect brain structure? A systematic and critical review of MRI findings

2009 ◽  
Vol 39 (11) ◽  
pp. 1763-1777 ◽  
Author(s):  
S. Navari ◽  
P. Dazzan
Keyword(s):  
Brain Structure ◽  
Structural Changes ◽  
Antipsychotic Drugs ◽  
Brain Magnetic Resonance Imaging ◽  
Specific Effect ◽  
Future Research ◽  
Antipsychotic Treatment ◽  
Mri Findings ◽  
Brain Volumes ◽  
The Brain

BackgroundThe potential effects of antipsychotic drugs on brain structure represent a key factor in understanding neuroanatomical changes in psychosis. This review addresses two issues: (1) do antipsychotic medications induce changes in total or regional human brain volumes and (2) do such effects depend on antipsychotic type?MethodA systematic review of studies reporting structural brain magnetic resonance imaging (MRI) measures: (1) directly in association with antipsychotic use; and (2) in patients receiving lifetime treatment with antipsychotics in comparison with drug-naive patients or healthy controls. We searched Medline and EMBASE databases using the medical subject heading terms: ‘antipsychotics’ AND ‘brain’ AND (MRI NOT functional). The search included studies published up to 31 January 2007. Wherever possible, we reported the effect size of the difference observed.ResultsThirty-three studies met our inclusion criteria. The results suggest that antipsychotics act regionally rather than globally on the brain. These volumetric changes are of a greater magnitude in association with typical than with atypical antipsychotic use. Indeed, there is evidence of a specific effect of antipsychotic type on the basal ganglia, with typicals specifically increasing the volume of these structures. Differential effects of antipsychotic type may also be present on the thalamus and the cortex, but data on these and other brain areas are more equivocal.ConclusionsAntipsychotic treatment potentially contributes to the brain structural changes observed in psychosis. Future research should take into account these potential effects, and use adequate sample sizes, to allow improved interpretation of neuroimaging findings in these disorders.

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