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Studies suggest the cholinergic system is involved in anesthesia-induced unconsciousness, hence unresponsiveness. A significant source of cholinergic innervation comes from basal forebrain cholinergic nuclei (BFCN), with bi-directional connections between anterior BFCN and the default mode network (DMN). Since DMN functional connectivity (FC) is consistently reduced during anesthesia-induced unresponsiveness in humans, we hypothesized that BFCN-FC during anesthesia-induced unresponsiveness is reduced and particularly, anterior BFCN-FC reductions might be related to DMN-FC reductions. Resting-state fMRI (rs-fMRI) signal correlations (i.e., a proxy for FC) were calculated. FC seeds were anterior and posterior BFCN and the DMN. Rs-fMRI data come from healthy male controls during wakefulness and anesthesia with sevoflurane (n = 15) (at fixed concentrations: 2 and 3 vol%) and propofol titrated to the endpoint of clinical unresponsiveness (n = 12), respectively. FC state differences were tested via paired t-tests; FC changes for anterior BFCN and DMN were associated via correlation analysis. We found reduced anterior and posterior BFCN-FC with sevoflurane and propofol compared to wakefulness. The correlation between reduced DMN-FC-and anterior BFCN-FC reductions was r = 0.57 (p = 0.01) for sevoflurane 3 vol%, r = 0.34 (p = 0.11) for sevoflurane 2 vol% and r = 0.47 (p = 0.06) for propofol. In summary, in this exploratory pilot study, we demonstrated reduced BFCN-FC and a potential correlation between reduced anterior BFCN-FC and DMN-FC during sevoflurane and propofol anesthesia. This suggests DMN changes as a potential factor of anterior BFCN-FC reductions during anesthesia-induced unresponsiveness and BFCN-FC reduction as a potential sign of such state.

Abstract Objective Cortical gray matter (GM) atrophy plays a central role in multiple sclerosis (MS) pathology. However, it is not commonly assessed in clinical routine partly because a number of methodological problems hamper the development of a robust biomarker to quantify GM atrophy. In previous work, we have demonstrated the clinical utility of the “mosaic approach” (MAP) to assess individual GM atrophy in the motor neuron disease spectrum and frontotemporal dementia. In this study, we investigated the clinical utility of MAP in MS, comparing this novel biomarker to existing methods for computing GM atrophy in single patients. We contrasted the strategies based on correlations with established biomarkers reflecting MS disease burden. Methods We analyzed T1‐weighted MPRAGE magnetic resonance imaging data from 465 relapsing‐remitting MS patients and 89 healthy controls. We inspected how variations of existing strategies to estimate individual GM atrophy (“standard approaches”) as well as variations of MAP (i.e., different parcellation schemes) impact downstream analysis results, both on a group and an individual level. We interpreted individual cortical disease burden as single metric reflecting the fraction of significantly atrophic data points with respect to the control group. In addition, we evaluated the correlations to lesion volume (LV) and Expanded Disability Status Scale (EDSS). Results We found that the MAP method yielded highest correlations with both LV and EDSS as compared to all other strategies. Although the parcellation resolution played a minor role in terms of absolute correlations with clinical variables, higher resolutions provided more clearly defined statistical brain maps which may facilitate clinical interpretability. Conclusion This study provides evidence that MAP yields high potential for a clinically relevant biomarker in MS, outperforming existing methods to compute cortical disease burden in single patients. Of note, MAP outputs brain maps illustrating individual cortical disease burden which can be directly interpreted in daily clinical routine.

Abstract Background Language deficits are cardinal manifestations of some frontotemporal dementia (FTD) phenotypes and also increasingly recognized in sporadic and familial amyotrophic lateral sclerosis (ALS). They have considerable social and quality‐of‐life implications, and adaptive strategies are challenging to implement. While the neuropsychological profiles of ALS–FTD phenotypes are well characterized, the neuronal underpinnings of language deficits are less well studied. Methods A multiparametric, quantitative neuroimaging study was conducted to characterize the involvement of language‐associated networks, tracts, and cortical regions with a panel of structural, diffusivity, and functional magnetic resonance imaging (MRI) metrics. Seven study groups were evaluated along the ALS–FTD spectrum: healthy controls (HC), individuals with ALS without cognitive impairment (ALSnci), C9orf72‐negative ALS–FTD, C9orf72‐positive ALS–FTD, behavioral‐variant FTD (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA). The integrity of the Broca's area, Wernicke's area, frontal aslant tract (FAT), arcuate fascicle (AF), inferior occipitofrontal fascicle (IFO), inferior longitudinal fascicle (ILF), superior longitudinal fascicle (SLF), and uncinate fascicle (UF) was quantitatively evaluated. The functional connectivity (FC) between Broca's and Wernicke’ areas and FC along the FAT was also specifically assessed. Results Patients with nfvPPA and svPPA exhibit distinctive patterns of gray and white matter degeneration in language‐associated brain regions. Individuals with bvFTD exhibit Broca's area, right FAT, right IFO, and UF degeneration. The ALSnci group exhibits Broca's area atrophy and decreased FC along the FAT. Both ALS–FTD cohorts, irrespective of C9orf72 status, show bilateral FAT, AF, and IFO pathology. Interestingly, only C9orf72‐negative ALS–FTD patients exhibit bilateral uncinate and right ILF involvement, while C9orf72‐positive ALS–FTD patients do not. Conclusions Language‐associated tracts and networks are not only affected in language‐variant FTD phenotypes but also in ALS and bvFTD. Language domains should be routinely assessed in ALS irrespective of the genotype.

BackgroundCocaine use disorder (CUD) is a global health issue with severe behavioral and cognitive sequelae. While previous evidence suggests a variety of structural and age-related brain changes in CUD, the impact on both, cortical thickness and brain age measures remains unclear.MethodsDerived from a publicly available data set (SUDMEX_CONN), 74 CUD patients and 62 matched healthy controls underwent brain MRI and behavioral-clinical assessment. We determined cortical thickness by surface-based morphometry using CAT12 and Brain Age Gap Estimate (BrainAGE) via relevance vector regression. Associations between structural brain changes and behavioral-clinical variables of patients with CUD were investigated by correlation analyses.ResultsWe found significantly lower cortical thickness in bilateral prefrontal cortices, posterior cingulate cortices, and the temporoparietal junction and significantly increased BrainAGE in patients with CUD [mean (SD) = 1.97 (±3.53)] compared to healthy controls (p

Objective: Developing an integrative approach to early treatment response classification using survival modeling and bioinformatics with various biomarkers for early assessment of filgrastim (granulocyte colony stimulating factor) treatment effects in amyotrophic lateral sclerosis (ALS) patients. Filgrastim, a hematopoietic growth factor with excellent safety, routinely applied in oncology and stem cell mobilization, had shown preliminary efficacy in ALS. Methods: We conducted individualized long-term filgrastim treatment in 36 ALS patients. The PRO-ACT database, with outcome data from 23 international clinical ALS trials, served as historical control and mathematical reference for survival modeling. Imaging data as well as cytokine and cellular data from stem cell analysis were processed as biomarkers in a non-linear principal component analysis (NLPCA) to identify individual response. Results: Cox proportional hazard and matched-pair analyses revealed a significant survival benefit for filgrastim-treated patients over PRO-ACT comparators. We generated a model for survival estimation based on patients in the PRO-ACT database and then applied the model to filgrastim-treated patients. Model-identified filgrastim responders displayed less functional decline and impressively longer survival than non-responders. Multimodal biomarkers were then analyzed by PCA in the context of model-defined treatment response, allowing identification of subsequent treatment response as early as within 3 months of therapy. Strong treatment response with a median survival of 3.8 years after start of therapy was associated with younger age, increased hematopoietic stem cell mobilization, less aggressive inflammatory cytokine plasma profiles, and preserved pattern of fractional anisotropy as determined by magnetic resonance diffusion tensor imaging (DTI-MRI). Conclusion: Long-term filgrastim is safe, is well-tolerated, and has significant positive effects on disease progression and survival in a small cohort of ALS patients. Developing and applying a model-based biomarker response classification allows use of multimodal biomarker patterns in full potential. This can identify strong individual treatment responders (here: filgrastim) at a very early stage of therapy and may pave the way to an effective individualized treatment option.

Abstract Background The development of permanent disability in multiple sclerosis (MS) is highly variable among patients, and the exact mechanisms that contribute to this disability remain unknown. Methods Following the idea that the brain has intrinsic network organization, we investigated changes of functional networks in MS patients to identify possible links between network reorganization and remission from clinical episodes in MS. Eighteen relapsing–remitting MS patients (RRMS) in their first clinical manifestation underwent resting-state functional MRI and again during remission. We used ten template networks, identified from independent component analysis, to compare changes in network coherence for each patient compared to those of 44 healthy controls from the Human Connectome Project test–retest dataset (two-sample t- test of pre-post differences). Combining a binomial test with Monte Carlo procedures, we tested four models of how functional coherence might change between the first clinical episode and remission: a network can change its coherence (a) with itself (“one-with-self”), (b) with another network (“one-with-other”), or (c) with a set of other networks (“one-with-many”), or (d) multiple networks can change their coherence with respect to one common network (“many-with-one”). Results We found evidence supporting two of these hypotheses: coherence decreased between the Executive Control Network and several other networks (“one-with-many” hypothesis), and a set of networks altered their coherence with the Cerebellar Network (“many-with-one” hypothesis). Conclusion Given the unexpected commonality of the Cerebellar Network’s altered coherence with other networks (a finding present in more than 70% of the patients, despite their clinical heterogeneity), we conclude that remission in MS may result from learning processes mediated by the Cerebellar Network.

Abstract Background Cortical thickness measures the width of gray matter of the human cortex. It can be calculated from T1-weighted magnetic resonance images (MRI). In group studies, this measure has been shown to correlate with the diagnosis/prognosis of a number of neurologic and psychiatric conditions, but has not been widely adapted for clinical routine. One of the reasons for this might be that there is no reference system which allows to rate individual cortical thickness data with respect to a control population. Methods To address this problem, this study compared different methods to assess statistical significance of cortical thinning, i.e. atrophy. All compared methods were nonparametric and encompassed rating an individual subject’s data set with respect to a control data population. Null distributions were calculated using data from the Human Connectome Project (HCP, n = 1000), and an additional HCP data set (n = 113) was used to calculate sensitivity and specificity to compare the different methods, whereas atrophy was simulated for sensitivity assessment. Validation measures were calculated for the entire cortex (“cumulative”) and distinct brain regions (“regional”) where possible. Results The approach yielding the highest combination of specificity and sensitivity implemented generating null distributions for anatomically distinct brain regions, based on the most extreme values observed in the population. With that method, while regional variations were observed, cumulative specificity of 98.9% and cumulative sensitivity at 80% was achieved for simulated atrophy of 23%. Conclusions This study shows that validated rating of individual cortical thickness measures is possible, which can help clinicians in their daily routine to discover signs of atrophy before they become visually apparent on an unprocessed MRI. Furthermore, given different pathologies present with distinct atrophy patterns, the regional validation proposed here allows to detect distinct patterns of atrophy, which can further enhance differential diagnosis/prognosis.

Abstract Background Frontotemporal dementias (FTD) include a genetically heterogeneous group of conditions with distinctive molecular, radiological and clinical features. The majority of radiology studies in FTD compare FTD subgroups to healthy controls to describe phenotype‐ or genotype‐associated imaging signatures. While the characterization of group‐specific imaging traits is academically important, the priority of clinical imaging is the meaningful interpretation of individual datasets. Methods To demonstrate the feasibility of single‐subject magnetic resonance imaging (MRI) interpretation, we have evaluated the white matter profile of 60 patients across the clinical spectrum of FTD. A z‐score‐based approach was implemented, where the diffusivity metrics of individual patients were appraised with reference to demographically matched healthy controls. Fifty white matter tracts were systematically evaluated in each subject with reference to normative data. Results The z‐score‐based approach successfully detected white matter pathology in single subjects, and group‐level inferences were analogous to the outputs of standard track‐based spatial statistics. Conclusions Our findings suggest that it is possible to meaningfully evaluate the diffusion profile of single FTD patients if large normative datasets are available. In contrast to the visual review of FLAIR and T2‐weighted images, computational imaging offers objective, quantitative insights into white matter integrity changes even at single‐subject level.

Imaging profiles from a longitudinal single-centre motor neuron disease study are presented. A standardized T1-weighted MRI protocol was implemented to characterise cortical disease burden trajectories across the UMN (upper motor neuron) - LMN (lower motor neuron) spectrum of motor neuron diseases (MNDs) (Tahedl et al., 2021). Patients with amyotrophic lateral sclerosis (ALS n = 61), patients with primary lateral sclerosis (PLS n = 23) and poliomyelitis survivors (PMS n = 45) were included. Up to four longitudinal scans were available for each patient, separated by an inter-scan-interval of four months. Individual and group-level cortical thickness profiles were appraised using a normalisation procedure with reference to subject-specific control groups. A z-scoring approach was utilised, where each patients’ cortex was first segmented into 1000 cortical regions, and then rated as ‘thin’, ‘thick’, or ‘comparable’ to the corresponding region of a demographically-matched control cohort. Fractions of significantly ‘thin’ and ‘thick’ patches were calculated across the entire cerebral vertex as well as in specific brain regions, such as the motor cortex, parietal, frontal and temporal cortices. This approach allows the characterisation of disease burden in individual subjects as well as at a group-level, both cross-sectionally and longitudinally. The presented framework may aid the interpretation of individual cortical disease burden in other patient cohorts.

Objective: Developing an integrative approach to early treatment response classification using survival modeling and bioinformatics with various biomarkers for early assessment of filgrastim (granulocyte colony stimulating factor) treatment effects in amyotrophic lateral sclerosis (ALS) patients. Filgrastim, a hematopoietic growth factor with excellent safety, routinely applied in oncology and stem cell mobilization, had shown preliminary efficacy in ALS.Methods: We conducted individualized long-term filgrastim treatment in 36 ALS patients. The PRO-ACT database, with outcome data from 23 international clinical ALS trials, served as historical control and mathematical reference for survival modeling. Imaging data as well as cytokine and cellular data from stem cell analysis were processed as biomarkers in a non-linear principal component analysis (NLPCA) to identify individual response.Results: Cox proportional hazard and matched-pair analyses revealed a significant survival benefit for filgrastim-treated patients over PRO-ACT comparators. We generated a model for survival estimation based on patients in the PRO-ACT database and then applied the model to filgrastim-treated patients. Model-identified filgrastim responders displayed less functional decline and impressively longer survival than non-responders. Multimodal biomarkers were then analyzed by PCA in the context of model-defined treatment response, allowing identification of subsequent treatment response as early as within 3 months of therapy. Strong treatment response with a median survival of 3.8 years after start of therapy was associated with younger age, increased hematopoietic stem cell mobilization, less aggressive inflammatory cytokine plasma profiles, and preserved pattern of fractional anisotropy as determined by magnetic resonance diffusion tensor imaging (DTI-MRI).Conclusion: Long-term filgrastim is safe, is well-tolerated, and has significant positive effects on disease progression and survival in a small cohort of ALS patients. Developing and applying a model-based biomarker response classification allows use of multimodal biomarker patterns in full potential. This can identify strong individual treatment responders (here: filgrastim) at a very early stage of therapy and may pave the way to an effective individualized treatment option.

Antimicrobial resistance is a major public health issue caused by antibiotic overuse and misuse. Antimicrobial stewardship (AMS) has been increasingly endorsed worldwide, but its effect has been studied scarcely in urologic settings. A before-after study was performed from 2018 through 2020 to evaluate changes in antimicrobial prescription, resistance rates and clinical safety upon implementation of an AMS audit and feedback program in the Urology Department of a large German academic medical center. The primary endpoints were safety clinical outcomes: the rate of infection-related readmissions and of infectious complications after transrectal prostate biopsies. Resistance rates and antimicrobial consumption rates were the secondary endpoints. The AMS team reviewed 196 cases (12% of all admitted in the department). The overall antibiotic use dropped by 18.7%. Quinolone prescriptions sank by 78.8% (p = 0.02) and 69.8% (p > 0.05) for ciprofloxacin and levofloxacin, respectively. The resistance rate of E. coli isolates declined against ceftriaxone (−9%), ceftazidime (−12%) and quinolones (−25%) in the AMS period. No significant increase in infection-related readmissions or infectious complications after prostate biopsies was observed (p = 0.42). Due to the potential to reduce antibiotic use and resistance rates with no surge of infection-related complications, AMS programs should be widely implemented in urologic departments.

The complexity of our actions and thinking is likely reflected in functional brain networks. Independent component analysis (ICA) is a popular data‐driven method to compute group differences between such networks. A common way to investigate network differences is based on ICA maps which are generated from study‐specific samples. However, this approach limits the generalizability and reproducibility of the results. Alternatively, network ICA templates can be used, but up to date, few such templates exist and are limited in terms of the functional systems they cover. Here, we propose a simple two‐step procedure to obtain ICA‐templates corresponding to functional brain systems of the researcher's choice: In step 1, the functional system of interest needs to be defined by means of a statistical parameter map (input), which one can generate with open‐source software such as NeuroSynth or BrainMap. In step 2, that map is correlated to group‐ICA maps provided by the Human Connectome Project (HCP), which is based on a large sample size and uses high quality and standardized acquisition procedures. The HCP‐provided ICA‐map with the highest correlation to the input map is then used as an ICA template representing the functional system of interest, for example, for subsequent analyses such as dual regression. We provide a toolbox to complete step 2 of the suggested procedure and demonstrate the usage of our pipeline by producing an ICA templates that corresponds to "motor function" and nine additional brain functional systems resulting in an ICA maps with excellent alignment with the gray matter/white matter boundaries of the brain. Our toolbox generates data in two different file formats: volumetric‐based (NIFTI) and combined surface/volumetric files (CIFTI). Compared to 10 existing templates, our procedure output component maps with systematically stronger contribution of gray matter to the ICA z‐values compared to white matter voxels in 9/10 cases by at least a factor of 2. The toolbox allows users to investigate functional networks of interest, which will enhance interpretability, reproducibility, and standardization of research investigating functional brain networks. [ABSTRACT FROM AUTHOR]

Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis.