BACKGROUND AND OBJECTIVES: Frontotemporal lobar degeneration (FTLD) as the second most common dementia encompasses a range of syndromes and often shows overlapping symptoms with other subtypes or neurodegenerative diseases, which poses a significant clinical diagnostic challenge. Recent advancements in artificial intelligence (AI), specifically the application of machine learning (ML) algorithms to neuroimaging, have significantly progressed in addressing this challenge. This study aims to assess the diagnostic and predictive efficacy of neuroimaging feature-based AI algorithms for FTLD. METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines. We searched Pubmed, Scopus, and Web of Science for English-language, peer-reviewed studies using the following three umbrella terms: artificial intelligence, frontotemporal lobar degeneration, and neuroimaging modality. Our survey focused on computer-aided diagnosis for FTLD, employing machine/deep learning with neuroimaging radiomic features. RESULTS: The meta-analysis includes 75 articles with 20,601 subjects, including 8,051 FTLD patients. The results reveal that FTLD can be automatically classified against healthy controls (HC) with pooled sensitivity and specificity of 86% and 89%, respectively. Likewise, FTLD versus Alzheimer's disease (AD) classification exhibits pooled sensitivity and specificity of 84% and 81%, while FTLD versus Parkinson's disease (PD) demonstrates pooled sensitivity and specificity of 84% and 75%, respectively. Classification performance distinguishing FTLD from atypical Parkinsonian syndromes (APS) showed pooled sensitivity and specificity of 84% and 79%, respectively. Multiclass classification sensitivity ranges from 42% to 100%, with lower sensitivity occurring in higher class distinctions (e.g., 5-class and 11-class). DISCUSSION: Our study demonstrates the effectiveness of utilizing neuroimaging features to distinguish FTLD from HC, AD, APS, and PD in binary classification. Utilizing deep learning with multimodal neuroimaging data to differentiate FTLD subtypes and perform multiclassification among FTLD and other neurodegenerative disease holds promise for expediting diagnosis. In sum, the meta-analysis supports translation of machine learning tools in combination with imaging to clinical routine paving the way to precision medicine.

• MeSH
• frontotemporální lobární degenerace * diagnostické zobrazování   MeSH
• lidé MeSH
• neurozobrazování * metody   normy   MeSH
• strojové učení MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• systematický přehled MeSH

• MeSH
• magnetická rezonanční tomografie MeSH
• nervový systém MeSH
• paměť MeSH
• počítačová simulace MeSH
• teoretické modely MeSH

• Konspekt
• Patologie. Klinická medicína
• NLK Obory
• neurologie
• radiologie, nukleární medicína a zobrazovací metody

Motor disability is a dominant and restricting symptom in multiple sclerosis, yet its neuroimaging correlates are not fully understood. We apply statistical and machine learning techniques on multimodal neuroimaging data to discriminate between multiple sclerosis patients and healthy controls and to predict motor disability scores in the patients. We examine the data of sixty-four multiple sclerosis patients and sixty-five controls, who underwent the MRI examination and the evaluation of motor disability scales. The modalities used comprised regional fractional anisotropy, regional grey matter volumes, and functional connectivity. For analysis, we employ two approaches: high-dimensional support vector machines run on features selected by Fisher Score (aiming for maximal classification accuracy), and low-dimensional logistic regression on the principal components of data (aiming for increased interpretability). We apply analogous regression methods to predict symptom severity. While fractional anisotropy provides the classification accuracy of 96.1% and 89.9% with both approaches respectively, including other modalities did not bring further improvement. Concerning the prediction of motor impairment, the low-dimensional approach performed more reliably. The first grey matter volume component was significantly correlated (R = 0.28-0.46, p < 0.05) with most clinical scales. In summary, we identified the relationship between both white and grey matter changes and motor impairment in multiple sclerosis. Furthermore, we were able to achieve the highest classification accuracy based on quantitative MRI measures of tissue integrity between patients and controls yet reported, while also providing a low-dimensional classification approach with comparable results, paving the way to interpretable machine learning models of brain changes in multiple sclerosis.

• MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• motorické poruchy * MeSH
• mozek diagnostické zobrazování   MeSH
• neurozobrazování MeSH
• postižení * MeSH
• roztroušená skleróza * diagnostické zobrazování   MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Developing sensitive and reliable methods to distinguish normal and abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture its prominent tool of persistent homology may benefit from going beyond analysing structural and functional connectivity to effective connectivity graphs capturing the direct causal interactions or information flows. Therefore, we assess the potential of persistent homology to directed brain network analysis by testing its discriminatory power in two distinctive examples of disease-related brain connectivity alterations: epilepsy and schizophrenia. We estimate connectivity from functional magnetic resonance imaging and electrophysiology data, employ Persistent Homology and quantify its ability to distinguish healthy from diseased brain states by applying a support vector machine to features quantifying persistent homology structure. We show how this novel approach compares to classification using standard undirected approaches and original connectivity matrices. In the schizophrenia classification, topological data analysis generally performs close to random, while classifications from raw connectivity perform substantially better; potentially due to topographical, rather than topological, specificity of the differences. In the easier task of seizure discrimination from scalp electroencephalography data, classification based on persistent homology features generally reached comparable performance to using raw connectivity, albeit with typically smaller accuracies obtained for the directed (effective) connectivity compared to the undirected (functional) connectivity. Specific applications for topological data analysis may open when direct comparison of connectivity matrices is unsuitable - such as for intracranial electrophysiology with individual number and location of measurements. While standard homology performed overall better than directed homology, this could be due to notorious technical problems of accurate effective connectivity estimation.

• MeSH
• elektroencefalografie MeSH
• epilepsie diagnostické zobrazování   patofyziologie   MeSH
• konektom * MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mapování mozku MeSH
• modely neurologické * MeSH
• mozek diagnostické zobrazování   patofyziologie   MeSH
• nervová síť diagnostické zobrazování   patofyziologie   MeSH
• schizofrenie diagnostické zobrazování   patofyziologie   MeSH
• záchvaty diagnostické zobrazování   patofyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

OBJECTIVES: Patients with schizophrenia have difficulties processing the emotional and cognitive states of others. Neuroimaging studies show inconsistent findings. METHODS: We used a Seed-based d Mapping meta-analytic method to explore brain activation during facial emotion recognition and theory of mind tasks in schizophrenia patients. RESULTS: The patients showed lesser recruitment of the facial emotion processing network; behavioural performance was associated with the activation of the precentral gyrus. We found abnormal activation of the mentalising network in schizophrenia patients during reasoning about other people's mental states; patients with worse performances showed lesser activation in the right insula and superior temporal gyrus. Multimodal meta-analysis showed overlaps of brain-related abnormalities for both modalities in schizophrenia, with reduced recruitment of the right insula, anterior cingulate and medial prefrontal cortex and increased activation in the bilateral parietal cortex. Meta-regression results indicate that illness duration, medication and symptomatology might influence social-cognitive network disruptions in schizophrenia. CONCLUSIONS: These findings suggest the complex impairment of social cognition, as demonstrated by neural-related circuit disruptions during facial emotion processing and theory of mind tasks in schizophrenia.

• MeSH
• emoce * MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mapování mozku MeSH
• neurozobrazování MeSH
• schizofrenie (psychologie) MeSH
• schizofrenie patofyziologie   MeSH
• sociální chování * MeSH
• teorie mysli * MeSH
• výraz obličeje MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

Kombinací diagnostických kritérií pro depresivní poruchu dostaneme více než 200 stavů, které mohou být nazvány depresivní poruchou. Současná vodítka léčby depresivní poruchy doporučují léčbu antidepresivy bez zhodnocení mozkové aktivity. Psychiatři potřebují nastroj, který by jim pomohl diferencovat depresivní syndrom a zvolit adekvátní léčbu pro individuálního pacienta. Skupinová analýza zobrazovacích vyšetření pacientů s depresivní poruchou není vhodná pro heterogenitu depresivní poruchy Individualizovaná analýza zobrazovacích dat může pomoci nalézt rozdílné neuronální fenotypy depresivní poruchy. Deset pacientů s rezistentní depresivní poruchou bylo vyšetřeno pomocí 18FDG PET v klidu a byla použita SPM analýza porovnávající každého jednotlivce vůči kontrolní skupině. U 8 pacientů jsme detekovali zvýšený metabolizmus v oblasti cuneu, u 5 pacientů pak zvýšení v oblasti amygdaly a mozečku. Snížený metabolizmus v oblasti dorsolaterální prefi-ontální kůry byl detekován u 7 pacientů. Individualizovaná PET analýza může být způsob, jak nalézt rozdílné neuronální fenotypy depresivního syndromu.

Major Depression is a syndrom. We can find more than 200 combinations that fullfill diagnostic criteria of Major Depression. Cu rrent guidelines recommend antidepressant therapy for treatment of Major Depression without any biological evaluation of brain activi ty. Psychiatrists need some tools to puzzle out depressive syndrome and find appropriate treatment for individual patient. Group an alyses of neuroimaging data of patients with Major Depression is not applicable due to its heterogenity. Individualized analyses of ne uroi- maging data can help to find various neuroimaging phenotypes of Major Depression. We assessed 10 patients with treatment resist ant depressive disorder using resting 18FDG PET and individual-to-group SPM analysis. We found increased metabolism in the cuneus i n 8 patients, in the amygdala and the cerebellum in 5 patients. Decreased metabolism in the dorsolateral prefrontal cortex were d etected in 7 patients. Individualized PET analyses can be the approach how to find different phenotypes of depressive syndrom.

• MeSH
• depresivní poruchy diagnóza   etiologie   metabolismus   MeSH
• fenotyp MeSH
• finanční podpora výzkumu jako téma MeSH
• financování organizované MeSH
• interpretace statistických dat MeSH
• klasifikace metody   MeSH
• lidé MeSH
• metabolické nemoci mozku diagnóza   epidemiologie   metabolismus   MeSH
• mozek - chemie genetika   MeSH
• pozitronová emisní tomografie metody   využití   MeSH
• terapie metody   trendy   využití   MeSH
• Check Tag
• lidé MeSH

Magnetic resonance spectroscopic imaging (MRSI) involves a huge number of spectra to be processed and analyzed. Several tools enabling MRSI data processing have been developed and widely used. However, the processing programs primarily focus on sophisticated spectra processing and offer limited support for the analysis of the calculated spectroscopic maps. In this paper the jSIPRO (java Spectroscopic Imaging PROcessing) program is presented, which is a java-based graphical interface enabling post-processing, viewing, analysis and result reporting of MRSI data. Interactive graphical processing as well as protocol controlled batch processing are available in jSIPRO. jSIPRO does not contain a built-in fitting program. Instead, it makes use of fitting programs from third parties and manages the data flows. Currently, automatic spectra processing using LCModel, TARQUIN and jMRUI programs are supported. Concentration and error values, fitted spectra, metabolite images and various parametric maps can be viewed for each calculated dataset. Metabolite images can be exported in the DICOM format either for archiving purposes or for the use in neurosurgery navigation systems.

• MeSH
• automatizované zpracování dat statistika a číselné údaje   MeSH
• Fourierova analýza MeSH
• funkční zobrazování neurálních procesů statistika a číselné údaje   MeSH
• lidé MeSH
• magnetická rezonanční tomografie statistika a číselné údaje   MeSH
• mozek metabolismus   patologie   MeSH
• programovací jazyk MeSH
• software * MeSH
• zobrazování trojrozměrné MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Imaging is pivotal in the evaluation and management of patients with seizure disorders. Elegant structural neuroimaging with magnetic resonance imaging (MRI) may assist in determining the etiology of focal epilepsy and demonstrating the anatomical changes associated with seizure activity. The high diagnostic yield of MRI to identify the common pathological findings in individuals with focal seizures including mesial temporal sclerosis, vascular anomalies, low-grade glial neoplasms and malformations of cortical development has been demonstrated. Positron emission tomography (PET) is the most commonly performed interictal functional neuroimaging technique that may reveal a focal hypometabolic region concordant with seizure onset. Single photon emission computed tomography (SPECT) studies may assist performance of ictal neuroimaging in patients with pharmacoresistant focal epilepsy being considered for neurosurgical treatment. This chapter highlights neuroimaging developments and innovations, and provides a comprehensive overview of the imaging strategies used to improve the care and management of people with epilepsy.

• MeSH
• elektroencefalografie MeSH
• epilepsie diagnostické zobrazování   MeSH
• lidé MeSH
• neurozobrazování * MeSH
• počítačové zpracování obrazu MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

• MeSH
• čelní lalok fyziologie   MeSH
• dospělí MeSH
• elektroencefalografie MeSH
• finanční podpora výzkumu jako téma MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• počítačové zpracování obrazu MeSH
• prsty ruky inervace   MeSH
• temenní lalok fyziologie   MeSH
• zobrazování trojrozměrné MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

• MeSH
• algoritmy MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• metaanalýza jako téma MeSH
• mladý dospělý MeSH
• mozková kůra diagnostické zobrazování   MeSH
• neurozobrazování MeSH
• počítačové zpracování obrazu metody   MeSH
• schizofrenie diagnostické zobrazování   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH