社论
Editorial: Focus feature on biomarkers
in network neuroscience
Linda Douw1, Mario Senden2,3, and Martijn van den Heuvel4,5
1Department of Anatomy and Neurosciences, Amsterdam Neuroscience, UMC, Vrije
Universiteit Amsterdam, 阿姆斯特丹, 荷兰
2Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience,
Maastricht University, 马斯特里赫特, 荷兰
3Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, 马斯特里赫特, 荷兰
4Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research,
Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 阿姆斯特丹, 荷兰
5Department of Child Psychiatry, Amsterdam UMC, Vrije
Universiteit Amsterdam, 阿姆斯特丹, 荷兰
关键词: Clinical neuroscience, Neurology, Psychiatry, Graph theory, Connectome, Treatment
监控, Response prediction, Personalized medicine, Precision medicine
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抽象的
There is an ongoing need for novel biomarkers in clinical neuroscience, as diagnosis of
neurological and psychiatric disorders is hampered by the pronounced overlap of behavioral
symptoms and other pathophysiological characteristics. The question that this Focus Feature
puts center stage is whether network-based biomarkers may provide a viable tool for
distinguishing between disordered populations or whether they may yield only limited
differentiating power because of largely shared network characteristics across conditions.
Multiple approaches exist to address the question of whether network-based biomarkers may
have clinical utility; we explore many of these in this Focus Feature. Blomsma and colleagues
(Blomsma et al., 2022) focus on the minimum spanning tree (MST) as a tool that potentially
unearths differences between neurological or psychiatric pathologies and healthy controls.
The authors propose that the MST offers a theoretical approach towards thresholding adja-
cency matrices, by including the subset of edges that maximize connectivity while not forming
any loops or cycles. They perform a systematic review on MST studies using different network
sizes and assess disease specificity and transdiagnostic sensitivity of MST-based metrics.
Although contradictions were present in results across studies, several trends could neverthe-
less be distilled within and across disease categories. 有趣的是, it became clear that the
MST measure of leaf fraction depends on network size independent of disease population,
limiting the interpretability of this measure across studies with different methodologies. 这
authors also suggest that reporting guidelines are needed in network neuroscience, emphasi-
zing the importance of always reporting numeric values for network metrics as well as the
analysis tools used as completely as possible.
The work by Rodriguez-Cruces and colleagues (Rodriguez-Cruces et al., 2022) uses a dif-
ferent strategy towards biomarker exploration: They reviewed the current state of network neu-
roscience literature in three particular epilepsy syndromes that are difficult to differentiate,
with the hope to identify shared as well as syndrome-specific network substrates. 的确,
the authors conclude that “brain network measures may ultimately serve as powerful
开放访问
杂志
引文: Douw, L。, Senden, M。, & van
den Heuvel, 中号. (2022). Editorial: Focus
feature on biomarkers in network
神经科学. 网络神经科学,
6(2), 298–300. https://doi.org/10.1162
/netn_e_00249
DOI:
https://doi.org/10.1162/netn_e_00249
已收到: 4 四月 2022
利益争夺: 作者有
声明不存在竞争利益
存在.
通讯作者:
Linda Douw
l.douw@amsterdamumc.nl
Handlng Editor:
奥拉夫·斯波恩斯
版权: © 2022
麻省理工学院
在知识共享下发布
归因 4.0 国际的
(抄送 4.0) 执照
麻省理工学院出版社
Editorial
intermediary phenotypes to study effects of biological as well as environmental factors on cog-
nitive systems in epileptic patients, including medication effects, disease status, and baseline
genetic factors,” indicating that there are indeed common and specific network markers for
these syndromes. They recommend future work to incorporate not only multimodal imaging,
but also genetic testing as well as rigorous clinical phenotyping. Together with multisite data
收藏, the authors posit that such work could pave the way towards incorporation of
network-based biomarkers in clinical practice in epilepsy.
This Focus Feature also provides and evaluates new network-based biomarkers. Kulik and
同事 (Kulik et al., 2022) explore associations between structural and functional connec-
tivity in a cohort of multiple sclerosis (MS) 患者, and investigate whether (alterations in) 这
relation could be a useful biomarker for cognitive impairment in MS. Although there were
significant differences in structure-function coupling between cognitively impaired patients
and matched healthy controls, receiver operating characteristic (ROC) curves revealed that
these group-level differences did not significantly differentiate between groups. This result
underlines the importance of reporting classification accuracy for potential biomarkers, 参与-
ularly in the context of significant group differences.
Iraji and colleagues (Iraji et al., 2022) use the newly defined method of multiscale indepen-
dent component analysis (msICA) to study shared and specific connectivity patterns across
males and females in schizophrenia. This method allows for data-driven natural detection
of functional sources across different spatial scales. Using three large cohorts, 作者
report on shared network differences between male and female schizophrenic patients, 但
also reveal sex-specific effects that correlated with symptom scores. The authors emphasize
the importance of carefully incorporating sex in the development of diagnostic, predictive,
and/or monitoring biomarkers in schizophrenia.
最后, Scheijbeler and colleagues (Scheijbeler et al., 2022) introduce a network version of
permutation entropy as a novel biomarker for early-stage Alzheimer’s disease (广告). This mea-
sure integrates information on local signal variability and complexity with nonlinear coupling.
They indeed find group differences between patients with early AD and a control population.
而且, classification accuracy through ROC curve analysis was comparable to the current
state-of-the-art biomarker in this context, namely relative theta power. Future work may aim to
replicate this finding in larger samples.
There is promise for network-based biomarkers in clinical neuroscience. An important step
towards adequate investigation of brain network measures as biomarkers is to consistently
report reliability, reproducibility, 灵敏度, and specificity of such measures. The increasing
availability of large databases with multimodal data in order to crosslink different approaches
will aid development of biomarkers suitable for differential diagnosis and prognosis. 我们
hope that the work included in this Focus Feature may inform future studies into this impor-
tant topic.
参考
Blomsma, N。, de Rooy, B., Gerritse, F。, van der Spek, R。, Tewarie,
P。, 希勒布兰德, A。, … van Dellen, 乙. (2022). Minimum spanning
tree analysis of brain networks: A systematic review of network
size effects, sensitivity for neuropsychiatric pathology and disor-
der specificity. 网络神经科学, 6(2), 301–319. https://土井
.org/10.1162/netn_a_00245
Iraji, A。, Faghiri, A。, 福, Z。, Rachakonda, S。, Kochunov, P。, Belger,
A。, … Calhoun, V. D. (2022). Multispatial-scale dynamic
interactions between functional sources reveal sex-specific
changes in schizophrenia. 网络神经科学, 6(2), 357–381.
https://doi.org/10.1162/netn_a_00196
Kulik, S. D ., Nauta, 我. M。, Tewarie, P。, Koubiyr, 我。, van Dellen, E.,
Ruet, A。, … Schoonheim, 中号. 中号. (2022). Structure-function
coupling as a correlate and potential biomarker of cognitive
impairment in multiple sclerosis. 网络神经科学, 6(2),
339–356. https://doi.org/10.1162/netn_a_00226
网络神经科学
299
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Editorial
Rodriguez-Cruces, R。, Royer, J。, Larivière, S。, Bassett, D. S。,
Caciagli, L。, & Bernhardt, 乙. C. (2022). Multimodal connectome
biomarkers of cognitive and affective dysfunction in the common
epilepsies. 网络神经科学, 6(2), 320–338. https://doi.org
/10.1162/netn_a_00237
Scheijbeler, 乙. P。, van Nifterick, A. M。, 斯塔姆, C. J。, 希勒布兰德, A。,
Gouw, A. A。, & de Haan, 瓦. (2022). Network-level permutation
entropy of resting-state MEG recordings: A novel biomarker for
early-stage Alzheimer’s disease? 网络神经科学, 6(2),
382–400. https://doi.org/10.1162/netn_a_00224
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网络神经科学
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