Biology subjects

Finger, E.

Publications and source records attributed to Finger, E..

11 recordsLinked to original sources

bioRxiv2026

Machine-learning MRI stratification of genetic frontotemporal dementia for clinical trial enrichment

BACKGROUNDGenetic frontotemporal dementia (FTD) shows large differences in symptom profiles, brain atrophy patterns, and progression rate, making clinical trials difficult to design and power. There is a need for biomarkers that can model disease progression, identify biologically distinct groups, and support efficient trial enrichment. METHODSWe applied contrastive trajectory inference (cTI), a machine-learning method, to structural MRI, white matter hyperintensity, and demographic data from 736 participants in the GENFI cohort, including non-carriers and carriers of C9orf72, GRN, or MAPT mutations. cTI produced an individual "genetic FTD progression score" (0-1) and grouped mutation carriers into data-driven subtypes. We tested construct validity using correlations between progression score and cognitive/functional measures, examined subtype differences in brain-behavior coupling, plasma neurofilament light (NfL), and longitudinal decline, and compared cTI-based trial enrichment against age, cortical thickness and NfL using analytic and simulation-based power analyses. RESULTSGenetic FTD progression scores correlated strongly with global dementia severity and multiple cognitive domains (all p < 0.001), confirming robust clinical scoring. Two mutation-carrier subtypes emerged: a Progressive Track (Subtype 2) with strong associations between progression score and cognitive/functional impairment, rising NfL, and faster longitudinal decline; and a Dissociated Track (Subtype 3) with comparable levels of structural variation but weak or absent clinical and NfL changes, suggesting relative biological stability. Baseline subtype membership added prognostic value for future decline in processing speed and language beyond baseline severity. Notably, for C9orf72 and GRN, cTI-informed enrichment reduced required recruited sample size per arm by about 61-75% compared with unenriched designs, and outperformed enrichment using age, cortical thickness or NfL in both analytic and simulation-based power analyses. CONCLUSIONSMachine-learning stratification of genetic FTD reveals a progressive and a dissociated disease track and provides individualized progression scores that closely track clinical status. cTI progression scores offer a powerful tool for trial enrichment, enabling smaller, more efficient prevention and early-intervention trials than conventional MRI or NfL markers alone.

neuroscience↗

bioRxiv2025

MRI-based classifier to identify close-to-onset cases in C9orf72 genetic frontotemporal dementia

Predicting symptom onset in genetic frontotemporal dementia (FTD) is crucial for advancing targeted interventions and clinical trial design. Brain changes begin years before clinical symptoms emerge, making neuroimaging a strong candidate for onset prediction. However, FTD is highly heterogeneous, encompassing diverse molecular pathologies, affected brain networks, and symptom trajectories. This variability limits the predictive power of any single imaging biomarker and underscores the need for an integrative, multimodal approach to improve prediction accuracy and generalizability. We used machine learning to integrate diverse neuroimaging features, identifying a robust signature for risk stratification. We analyzed T1-weighted and T2-weighted MRI scans from 71 symptomatic C9orf72 carriers, 90 presymptomatic carriers, and 69 healthy controls from the GENFI cohort. We used FreeSurfer to measure cortical thickness and subcortical volumes, and BISON to quantify white matter hyperintensities (WMH). We applied Principal Component Analysis for dimensionality reduction and trained a random forest classifier to distinguish symptomatic carriers from controls. The model was subsequently applied to the presymptomatic cohort to identify individuals whose brain patterns resembled those of symptomatic cases, under the hypothesis that greater similarity indicated a higher risk of conversion. We validated the model with neuropsychological data and a two-year longitudinal follow-up. The classifier distinguished symptomatic C9orf72 carriers from controls with 87.0% accuracy. When applied to presymptomatic carriers, the model identified 21.1% of the cohort as having brain features comparable to those of symptomatic cases. This "high-risk group" showed significant neuropsychological weaknesses in executive function, language and social cognition compared to the non high-risk group. The model accurately predicted clinical conversion within a two-year period with 84.5% accuracy, a 70% sensitivity and a 93.3% negative predictive value. Our findings demonstrate the utility of a machine learning approach using multi-modal MRI to identify presymptomatic C9orf72 carriers at high risk of disease onset within the next two years. By capturing subtle neuroanatomical patterns associated with disease processes, this approach offers a promising method for stratifying genetic FTD carriers prior to symptom onset. Such predictive models could optimize patient selection in future clinical trials.

neuroscience↗

bioRxiv2025

White Matter Hyperintensities Precede other Biomarkers in GRN Frontotemporal Dementia

INTRODUCTIONIncreased white matter hyperintensities (WMHs) have been reported in genetic frontotemporal dementia (FTD) in small studies, but the sequence of WMH abnormalities relative to other biomarkers is unclear. METHODSUsing a large dataset (n=763 GENFI2 participants), we measured WMHs and examined them across genetic FTD variants and stages. Cortical and subcortical volumes were parcellated, and serum neurofilament light chain (NfL) levels were measured. Biomarker progression was assessed with discriminative event-based and regression modeling. RESULTSSymptomatic GRN carriers showed elevated WMHs, primarily in the frontal lobe, while no significant increase was observed in C9orf72 or MAPT carriers. WMH abnormalities preceded NfL elevation, ventricular enlargement, and cortical atrophy. Longitudinally, baseline WMHs predicted subcortical changes, while subcortical volumes did not predict WMH changes, suggesting WMHs may precede neurodegeneration. DISCUSSIONWMHs are elevated in a subset of GRN-related FTD. When present, they appear early and should be considered in disease progression models. HighlightsO_LIElevated WMH volumes in symptomatic GRN carriers, but not in other mutations. C_LIO_LIWMH accumulation is mostly observed in the frontal lobe. C_LIO_LIWMH abnormalities appear early in GRN-FTD, before NfL, atrophy, and ventriculomegaly. C_LIO_LILongitudinally, WMH volumes can predict subcortical changes, but not vice versa. C_LIO_LIWMHs are key early markers in GRN-FTD and should be included in progression models. C_LI RESEARCH IN CONTEXTO_ST_ABSSystematic reviewC_ST_ABSWe systematically reviewed the literature on white matter hyperintensities (WMHs) in frontotemporal dementia (FTD) using PubMed. While a few small studies reported increased WMHs in GRN mutation carriers, their sample sizes were limited, and they did not assess the timing of WMHs within disease progression or their temporal relationship to other biomarkers. InterpretationWe identified a sequence of key biomarkers in GRN-related FTD and demonstrated that WMHs are among the earliest biomarkers, preceding cortical and subcortical atrophy as well as blood biomarkers. This aligns with neuropathological evidence of early white matter involvement in FTLD-GRN. Additionally, using a larger dataset, we validated previous reports of elevated WMHs in GRN carriers, confirming their reliability. Future directionsFuture studies should integrate WMHs into FTD progression models to enhance early diagnosis. Understanding why only a subset of GRN carriers exhibit high WMH volumes remains a key research priority.

neuroscience↗

bioRxiv2021

Cerebellar and subcortical atrophy contribute to psychiatric symptoms in frontotemporal dementia

Recent studies have suggested that cerebellar and subcortical structures are impacted early in the disease progression of genetic frontotemporal dementia (FTD) due to microtubule-associated protein tau (MAPT), progranulin (GRN) and chromosome 9 open reading frame 72 (C9orf72). However, the clinical contribution of the structures involved in the cerebello-subcortical circuitry has been understudied in FTD given their potentially central role in cognition and behaviour processes. The present study aims to investigate whether there is an association between the atrophy of the cerebellar and subcortical structures, and neuropsychiatric symptoms (using the revised version of the Cambridge Behavioral Inventory, CBI-R) across genetic mutations and whether this association starts during the preclinical phase of the disease. Our study included 983 participants from the Genetic Frontotemporal dementia Initiative (GENFI) including mutation carriers (n=608) and non-carrier first-degree relatives of known symptomatic carriers (n= 375). Voxel-wise analysis of the thalamus, striatum, globus pallidus, amygdala, and the cerebellum was performed using deformation based morphometry (DBM) and partial least squares analyses (PLS) were used to link morphometry and behavioural symptoms. Our univariate results suggest that in this group of primarily presymptomatic subjects, volume loss in subcortical and cerebellar structure was primarily a function of aging, with only the C9orf72 group showing more pronounced volume loss in the thalamus compared to the non-carrier individuals. PLS analyses demonstrated that the cerebello-subcortical circuitry is related to all neuropsychiatric symptoms from the CBI-R, with significant overlap in brain/behaviour patterns, but also specificity for each genetic group. The biggest differences were in the extent of the cerebellar involvement (larger extent in C9orf72 group) and more prominent amygdalar contribution in the MAPT group. Finally, our findings demonstrated that C9orf72 and MAPT brain scores were related to estimated years before the age of symptom onset (EYO) in a second order relationship highlighting a steeper brain score decline 20 years before expected symptom onset, while GRN brain scores were related to age and not EYO. Overall, these results demonstrated the important role of the subcortical structures and especially of the cerebellum in genetic FTD symptom expression.

neuroscience↗

bioRxiv2021

Global network structure and local transcriptomic vulnerability shape atrophy in sporadic and genetic behavioral variant frontotemporal dementia

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioral variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). We first identify distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbors, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicenter of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and anteromedial temporal areas. Finally, we find that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, effectively modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability.

neuroscience↗

bioRxiv2021

CALIPER: A software for blood-free parametric Patlak mapping using PET/MRI input function.

Routine clinical use of absolute PET quantification techniques is limited by the need for serial arterial blood sampling for input function and more importantly by the lack of automated pharmacokinetic analysis tools that can be readily implemented in clinic with minimal effort. PET/MRI provides the ability for absolute quantification of PET probes without the need for serial arterial blood sampling using image-derived input functions (IDIFs). Here we introduce caliPER, a modular and scalable software for simplified pharmacokinetic modelling of PET probes with irreversible uptake or binding based on PET/MR IDIFs and Patlak Plot analysis. caliPER generates regional values or parametric maps of net influx rate (Ki) using reconstructed dynamic PET images and anatomical MRI aligned to PET for IDIF vessel delineation. We evaluated the performance of caliPER for blood-free region-based and pixel-wise Patlak analyses of [18F] FDG by comparing caliPER IDIF to serial arterial blood input functions and its application in imaging brain glucose hypometabolism in Frontotemporal dementia. IDIFs corrected for partial volume errors including spill-out and spill-in effects were similar to arterial blood input functions with a general bias of around 6-8%, even for arteries <5 mm. The Ki and cerebral metabolic rate of glucose estimated using caliPER IDIF were similar to estimates using arterial blood sampling (<2%) and within limits of whole brain values reported in literature. Overall, caliPER is a promising tool for irreversible PET tracer quantification and can simplify the ability to perform parametric analysis in clinical settings without the need for blood sampling. HighlightsO_LIcaliPER is an adaptable image processing software for extracting image-derived input functions and generating parametric maps of irreversible PET tracer uptake. C_LIO_LIAnatomical (T1-weighted/T2-weighted/time-of-flight angiography) MRI carefully aligned to PET provides a robust approach for delineation of vessels on PET, eliminating the need for serial blood sampling for input functions. C_LIO_LIApplication of caliPER in modelling glucose uptake in patients with Frontotemporal dementia, demonstrates the feasibility of absolute quantification of cerebral metabolic rate of glucose in clinical populations. C_LI

bioinformatics↗

bioRxiv2020

Severe murine schistosomiasis results from disruption of the modulation of anti-egg CD4 T cell response by immunodominance of a single egg epitope

This study exploits the consistent correlation between immunodominance of the major egg antigen Sm-p40234-246, a robust Th1/Th17 anti-egg CD4 response and severe liver immunopathology in experimental murine schistosomiasis as an experimental platform to analyze how different degrees of immunodominance affect CD4 modulation and disease outcome. The results show that strong immunodominance of a restricted egg epitope repertoire skews CD4 modulation towards a pathogenic Th1/Th17 pro-inflammatory response and that neutralizing this immunodominance generates an opposite and restorative effect. These results identify immunodominance as an important pathogenic component that influences CD4 modulation in experimental murine schistosomiasis and can be manipulated to treat this and maybe other CD4 mediated diseases. SummaryAntigen informed CD4 modulation determines how efficiently the immune system neutralizes a threat; however, this process and its components are not fully comprehended. This study analyzes immunodominance as one component able to disrupt CD4 modulation and turn pathogenic an otherwise healthy immune response.

immunology↗

bioRxiv2019

Data-driven algorithm for the diagnosis of behavioral variant frontotemporal dementia

INTRODUCTIONBrain structural imaging is paramount for the diagnosis of behavioral variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. METHODSA total of 515 subjects from two different bvFTD databases (training and validation cohorts) were included to perform voxel-wise deformation-based morphometry analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from morphometric differences in isolation and together with bedside cognitive scores. RESULTSAverage ten-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In a separate validation cohort of genetically confirmed bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added cognitive scores. DISCUSSIONThe random forest classifier developed can accurately predict bvFTD at the individual subject level.

neuroscience↗

bioRxiv2019

Analysis of brain atrophy and local gene expression implicates astrocytes in Frontotemporal dementia

Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder characterized by neuronal loss in the frontal and temporal lobes. Despite progress in understanding which genes are associated with the aetiology of FTD, the biological basis of how mutations in these genes lead to cell loss in specific cortical regions remains unclear. In this work we combined gene expression data for 16,772 genes from the Allen Institute for Brain Science atlas with brain maps of gray matter atrophy in symptomatic C9orf72, GRN and MAPT mutation carriers obtained from the Genetic FTD Initiative study. No significant association was seen between (C9orf2, GRN and MAPT expression and the atrophy patterns in the respective genetic groups. Between 1,000 and 5,000 genes showed a negative or positive correlation with the atrophy pattern within each individual genetic group, with the most significantly associated genes being TREM2, SSBP3 and GPR158 (negative association in C9orf72, GRN and MAPT respectively) and RELN, MXRA8 and LPA (positive association in C9orf72, GRN and MAPT respectively). An overrepresentation analysis identified a negative correlation with genes involved in mitochondrial function, and a positive correlation with genes involved in vascular and glial cell function in each of the genetic groups. After adjusting for spatial autocorrelation, a set of 423 and 700 genes showed significant positive and negative correlation, respectively, with atrophy patterns in all three maps. The gene set with increased expression in spared cortical regions was enriched for neuronal and microglial genes, while the gene set with increased expression in atrophied regions was enriched for astrocyte and endothelial cell genes. Our analysis suggests that these cell types may play a more active role in the onset of neurodegeneration in FTD than previously assumed, and in the case of the positively-associated cell marker genes, potentially through emergence of neurotoxic astrocytes and alteration in the blood-brain barrier respectively. Abbreviated summaryAltmann et al. investigated the concordance between spatial cortical gene expression in healthy subjects and atrophy patterns in genetic frontotemporal dementia. They found that elevated gene expression of endothelial cell and astrocyte-related genes in regions with atrophy, suggesting a role of these cell types in the aetiology of frontotemporal dementia.

neuroscience↗

bioRxiv2018

Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: applied to GENFI study

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimers disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure.\n\nIn this paper we propose a spatiotemporal analysis pipeline based Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects.\n\nWe found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

neuroscience↗

bioRxiv2017

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we present a new machine learning technique - Subtype and Stage Inference (SuStaIn) - able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available crosssectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal new subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes, and characterises within-group heterogeneity for the first time. In Alzheimers disease, SuStaIn uncovers three subtypes, uniquely revealing their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p=7.18x10--4) or temporal stage (p=3.96x10-5). SuStaIn thus offers new promise for enabling disease subtype discovery and precision medicine.

neuroscience↗