{"entity": "researcher", "timestamp": "2026-10-12T22:31:45.062Z", "family": "Franzmeier", "given": "Nicolai", "initials": "N", "orcid": "0000-0001-9736-2283", "affiliations": ["Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.", "Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.", "Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, M\u00f6lndal and Gothenburg, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/55b52b106c0c45ea8da2d14f52588fb3.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/55b52b106c0c45ea8da2d14f52588fb3"}}, "publications": [{"entity": "publication", "iuid": "62177735704141fe8dc121fbfe7ae723", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/62177735704141fe8dc121fbfe7ae723.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/62177735704141fe8dc121fbfe7ae723"}}, "title": "Age and Alzheimer's disease affect functional connectivity along separate axes of functional brain organization.", "authors": [{"family": "Rittmo", "given": "Jonathan", "initials": "J", "orcid": "0000-0001-5075-0166", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f46afd59d3a44a18a35c560c3d2e37f3.json"}}, {"family": "Franzmeier", "given": "Nicolai", "initials": "N", "orcid": "0000-0001-9736-2283", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/55b52b106c0c45ea8da2d14f52588fb3.json"}}, {"family": "Strandberg", "given": "Olof", "initials": "O"}, {"family": "Chauveau", "given": "L\u00e9a", "initials": "L"}, {"family": "Satterthwaite", "given": "Theodore D", "initials": "TD", "orcid": "0000-0001-7072-9399", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/461c3637946f4ed8956764658711fca7.json"}}, {"family": "Wisse", "given": "Laura Em", "initials": "LE"}, {"family": "Spotorno", "given": "Nicola", "initials": "N"}, {"family": "Behjat", "given": "Harry H", "initials": "HH"}, {"family": "Dehsarvi", "given": "Amir", "initials": "A", "orcid": "0000-0001-7116-9741", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3fd2869fe7464b4aa679ccb0db603347.json"}}, {"family": "van Westen", "given": "Danielle", "initials": "D"}, {"family": "Anij\u00e4rv", "given": "Toomas Erik", "initials": "TE"}, {"family": "Palmqvist", "given": "Sebastian", "initials": "S"}, {"family": "Janelidze", "given": "Shorena", "initials": "S"}, {"family": "Stomrud", "given": "Erik", "initials": "E"}, {"family": "Ossenkoppele", "given": "Rik", "initials": "R"}, {"family": "Mattsson-Carlgren", "given": "Niklas", "initials": "N"}, {"family": "Hansson", "given": "Oskar", "initials": "O"}, {"family": "Vogel", "given": "Jacob W", "initials": "JW"}], "type": "journal article", "published": "2025-05-25", "journal": {"title": "bioRxiv", "issn": "2692-8205", "issn-l": null}, "abstract": "Aging and Alzheimer's disease (AD) are associated with alterations in functional connectivity (FC), yet their spatial and temporal characteristics remain debated. Whole-cortex functional gradients, which organize regions along axes of functional similarity, are positioned here as a framework for understanding such alterations. Across two independent cohorts (BioFINDER-2, N=973; ADNI, N=129), we demonstrate that hyper- and hypoconnectivity in both healthy aging and biomarker-confirmed AD progression coexist systematically, forming consistent spatial patterns that align with two distinct axes of brain organization. Using a combination of longitudinal and non-linear analyses, we show that the early (but not late) stages of AD pathology accumulation is associated with functional alteration along the sensory-association axis, a pattern that vanishes in later stages of AD. However, functional alteration along the sensory-association axis is associated with worse cognition throughout the AD spectrum, and even in older adults without AD pathology, suggesting that these FC patterns may reflect a general neural response to cognitive strain. Independently of AD, older age was associated with alterations instead along the executive-nonexecutive axis, a finding that was consistent throughout the adult lifespan. These findings highlight the fundamental role of intrinsic functional organization in shaping how the brain responds to aging and to AD, helping to resolve previously reported discrepancies.", "doi": "10.1101/2025.05.22.655469", "pmid": "40661536", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12258722"}, {"db": "pii", "key": "2025.05.22.655469"}], "notes": [], "created": "2026-08-20T11:05:54.791Z", "modified": "2026-09-23T14:53:42.020Z"}, {"entity": "publication", "iuid": "fed777ef692d4f6291d96bdd9aa3ffbc", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/fed777ef692d4f6291d96bdd9aa3ffbc.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/fed777ef692d4f6291d96bdd9aa3ffbc"}}, "title": "Longer scans boost prediction and cut costs in brain-wide association studies.", "authors": [{"family": "Ooi", "given": "Leon Qi Rong", "initials": "LQR", "orcid": "0000-0002-3546-4580", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ebe0d31183d441339365d04b82de818e.json"}}, {"family": "Orban", "given": "Csaba", "initials": "C", "orcid": "0000-0001-9133-3561", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/71c09fa5d88e4647ada303902bc77a56.json"}}, {"family": "Zhang", "given": "Shaoshi", "initials": "S", "orcid": "0000-0002-6352-9150", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f736d5d1c9094b7daf5e97df2ab159d8.json"}}, {"family": "Nichols", "given": "Thomas E", "initials": "TE", "orcid": "0000-0002-4516-5103", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/849f3024103b4d0da2669e0fe0f9146a.json"}}, {"family": "Tan", "given": "Trevor Wei Kiat", "initials": "TWK"}, {"family": "Kong", "given": "Ru", "initials": "R", "orcid": "0000-0001-7842-0329", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a3d34947afd641299085d6ae1d951f4c.json"}}, {"family": "Marek", "given": "Scott", "initials": "S", "orcid": "0000-0003-0032-9052", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/98c7de99f8284f398cee7ee738ba0f67.json"}}, {"family": "Dosenbach", "given": "Nico U F", "initials": "NUF", "orcid": "0000-0002-6876-7078", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ec2c89111c574ccb8b95a377557bd521.json"}}, {"family": "Laumann", "given": "Timothy", "initials": "T", "orcid": "0000-0002-0428-427X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2758ff472db34d21b45fd3f207980776.json"}}, {"family": "Gordon", "given": "Evan M", "initials": "EM", "orcid": "0000-0002-2276-5237", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7cc0226201ae49159c4e6cffc5568c06.json"}}, {"family": "Yap", "given": "Kwong Hsia", "initials": "KH", "orcid": "0000-0001-9617-9970", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e681e7be593f4da1af5f054cf9c11946.json"}}, {"family": "Ji", "given": "Fang", "initials": "F", "orcid": "0000-0002-7471-0115", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c03925f224f64b129e795852e232e87f.json"}}, {"family": "Chong", "given": "Joanna Su Xian", "initials": "JSX", "orcid": "0009-0008-5077-5206", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d84ef108db8d401cb2582d28fb94b390.json"}}, {"family": "Chen", "given": "Christopher", "initials": "C", "orcid": "0000-0002-1047-9225", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1d8a33c761bb445987ed28b8e580d007.json"}}, {"family": "An", "given": "Lijun", "initials": "L", "orcid": "0000-0003-1030-4625", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06744ec5a4ad4223b88038071e7b1dbf.json"}}, {"family": "Franzmeier", "given": "Nicolai", "initials": "N", "orcid": "0000-0001-9736-2283", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/55b52b106c0c45ea8da2d14f52588fb3.json"}}, {"family": "Roemer", "given": "Sebastian Niclas", "initials": "SN", "orcid": "0000-0002-1397-7297", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1d8510c578c346cb9560eceb6ba35892.json"}}, {"family": "Hu", "given": "Qingyu", "initials": "Q"}, {"family": "Ren", "given": "Jianxun", "initials": "J", "orcid": "0000-0002-7885-4546", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cd4693bf8b05444190c8557b7bc0a20e.json"}}, {"family": "Liu", "given": "Hesheng", "initials": "H", "orcid": "0000-0002-7233-1509", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b35de5f9f6054101b90bc92d95d66423.json"}}, {"family": "Chopra", "given": "Sidhant", "initials": "S", "orcid": "0000-0003-0866-3477", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/97fc5773d7cd4754896142d30e8759d3.json"}}, {"family": "Cocuzza", "given": "Carrisa V", "initials": "CV", "orcid": "0000-0001-5117-2433", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7611a9cb31574a8e899e78ea21a1826b.json"}}, {"family": "Baker", "given": "Justin T", "initials": "JT", "orcid": "0000-0002-6629-2195", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b7d0cec8561645d99f12da5876be9f2d.json"}}, {"family": "Zhou", "given": "Juan Helen", "initials": "JH", "orcid": "0000-0002-0180-8648", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6bab835f57e34342af06d01dfcd71655.json"}}, {"family": "Bzdok", "given": "Danilo", "initials": "D", "orcid": "0000-0003-3466-6620", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/27ee0d69626c43929e03d75fcaa92c12.json"}}, {"family": "Eickhoff", "given": "Simon B", "initials": "SB", "orcid": "0000-0001-6363-2759", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/adade102f594420dba34773923828c24.json"}}, {"family": "Holmes", "given": "Avram J", "initials": "AJ", "orcid": "0000-0001-6583-803X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5b2ffbe9d0584d48a71cdfdaf45f181f.json"}}, {"family": "Yeo", "given": "B T Thomas", "initials": "BTT", "orcid": "0000-0002-0119-3276", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/115d5e2815cb478da50216d86075afad.json"}}, {"family": "Alzheimer\u2019s Disease Neuroimaging Initiative", "given": "", "initials": ""}], "type": "journal article", "published": "2025-03-11", "journal": {"title": "bioRxiv", "issn": "2692-8205", "issn-l": null}, "abstract": "A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional MRI (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size \u00d7 scan time per participant). The model explains empirical prediction accuracies extremely well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R2 = 0.89), spanning a wide range of scanners, acquisitions, racial groups, disorders and ages. For scans \u226420 mins, prediction accuracy increases linearly with the logarithm of total scan duration, suggesting interchangeability of sample size and scan time. However, sample size is ultimately more important than scan time in determining prediction accuracy. Nevertheless, when accounting for overhead costs associated with each participant (e.g., recruitment costs), to boost prediction accuracy, longer scans can yield substantial cost savings over larger sample size. To achieve high prediction performance, 10-min scans are highly cost inefficient. In most scenarios, the optimal scan time is \u226520 mins. On average, 30-min scans are the most cost-effective, yielding 22% cost savings over 10-min scans. Overshooting is cheaper than undershooting the optimal scan time, so we recommend aiming for \u226530 mins. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-cortical BWAS. Standard power calculations maximize sample size at the expense of scan time. Our study demonstrates that optimizing both sample size and scan time can boost prediction power while cutting costs. Our empirically informed reference is available for future study planning: WEB_APPLICATION_LINK.", "doi": "10.1101/2024.02.16.580448", "pmid": "38405815", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10889017"}, {"db": "pii", "key": "2024.02.16.580448"}], "notes": [], "created": "2026-09-23T13:44:10.636Z", "modified": "2026-09-23T13:44:11.138Z"}, {"entity": "publication", "iuid": "244e55a1fe3f4cc689483a2f4bbe8d29", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/244e55a1fe3f4cc689483a2f4bbe8d29.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/244e55a1fe3f4cc689483a2f4bbe8d29"}}, "title": "Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight.", "authors": [{"family": "Vogel", "given": "Jacob W", "initials": "JW"}, {"family": "Corriveau-Lecavalier", "given": "Nick", "initials": "N", "orcid": "0000-0002-6561-9870", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/61f1bca2c0d64bc4927b2d7ed4f75139.json"}}, {"family": "Franzmeier", "given": "Nicolai", "initials": "N", "orcid": "0000-0001-9736-2283", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/55b52b106c0c45ea8da2d14f52588fb3.json"}}, {"family": "Pereira", "given": "Joana B", "initials": "JB"}, {"family": "Brown", "given": "Jesse A", "initials": "JA"}, {"family": "Maass", "given": "Anne", "initials": "A"}, {"family": "Botha", "given": "Hugo", "initials": "H"}, {"family": "Seeley", "given": "William W", "initials": "WW"}, {"family": "Bassett", "given": "Dani S", "initials": "DS", "orcid": "0000-0002-6183-4493", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1df60e7e90a942ffb8e97713221f7f5e.json"}}, {"family": "Jones", "given": "David T", "initials": "DT", "orcid": "0000-0002-4807-9833", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/dcc287a740324b4cbe89fdcd0c6dba04.json"}}, {"family": "Ewers", "given": "Michael", "initials": "M", "orcid": "0000-0001-5231-1714", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d2368c66c8ea44e4a04f798487a6242a.json"}}], "type": "journal article", "published": "2023-10-00", "journal": {"title": "Nat Rev Neurosci", "issn": "1471-0048", "issn-l": null, "volume": "24", "issue": "10", "pages": "620-639"}, "abstract": "Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.", "doi": "10.1038/s41583-023-00731-8", "pmid": "37620599", "labels": {"Jacob W Vogel": null, "DDLS Fellow": null}, "xrefs": [{"db": "pii", "key": "10.1038/s41583-023-00731-8"}], "notes": [], "created": "2023-10-25T16:52:17.842Z", "modified": "2023-11-29T06:41:38.564Z"}]}