Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference.
Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference.
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DOI：
10.1073/pnas.2203020119
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发表时间：
2022-08-09
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
--
作者：
自早期报道局灶性损伤导致行为改变以来，将认知功能定位到不同的大脑区域一直是人类大脑研究的主要内容。然而，越来越多的证据表明，各地区并非孤立行动。在这里，我们通过在真实连接体数据（1,000名受试者执行7项任务）中比较本地化与大规模统计程序的性能，来评估本地化主义观点的实际意义。我们发现，与简单的大规模程序相比，流行的本地化程序实质上错过了更多的真实效果。通过强调简单替代方法的力量，我们认为超越定位是可行的，并且可以帮助解锁人类神经科学发现的机会。神经影像学的推断通常发生在局灶脑区或回路的水平上。然而，越来越多的有力研究描绘了分布在整个大脑的广泛影响的更丰富的图景，这表明许多焦点报告可能只反映了潜在影响的冰山一角。焦点视角与大尺度视角如何影响我们做出的推论，尚未使用实际数据进行全面评估。在这里，我们使用经验基准程序比较了代表多个推理水平的程序的敏感性和特异性，该程序从人类连接组项目数据集（约1,000名受试者，7个任务，3个重新采样组大小，7个推理程序）中重新采样基于任务的连接组。只有大尺度（网络和全脑）程序获得传统的80%的统计功率水平来检测平均效应，反映出比焦点（边缘和簇）程序多出bb0 - 20%的统计功率。与家庭错误率控制程序相比，错误发现率的能力也大大提高。缺点是相当有限的；与权力的增加相比，大范围和FDR手术的特异性损失相对较小。此外，我们介绍的大规模方法简单，快速，易于使用，为研究人员提供了一个直接的起点。这也表明，不仅在功能连接方面，而且在相关领域，包括基于任务的激活方面，有可能出现更复杂、更大规模的方法。总之，这项工作表明，转移推理规模和选择FDR控制都是可以立即实现的，并且可以帮助纠正困扰该领域典型研究的统计能力问题。
Localizing cognitive function to distinct brain areas has been a mainstay of human brain research since early reports that focal injuries produce changes in behavior. Yet, accumulating evidence shows that areas do not act in isolation. Here, we evaluate the practical implications of the localizationist perspective by comparing the performance of localizing versus broad-scale statistical procedures in real connectome data (1,000 subjects performing 7 tasks). We find that popular localizing procedures miss substantially more true effects than simple broad-scale procedures. By highlighting the power of simple alternatives, we argue that moving beyond localization is viable and can help unlock opportunities for human neuroscience discovery. Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting >20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate– compared with familywise error rate–controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.
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影响因子：
4.8
作者：
Lin, Qiu-Hua;Liu, Jingyu;Zheng, Yong-Rui;Liang, Hualou;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
3.7
作者：
Cremers HR;Wager TD;Yarkoni T
通讯作者：
Yarkoni T
DOI：
10.1073/pnas.1121049109
发表时间：
2012-04-03
影响因子：
11.1
作者：
Gonzalez-Castillo, Javier;Saad, Ziad S.;Bandettini, Peter A.
通讯作者：
Bandettini, Peter A.
影响因子：
4.8
作者：
Gao S;Mishne G;Scheinost D
通讯作者：
Scheinost D
影响因子：
64.8
作者：
Marek S;Tervo-Clemmens B;Calabro FJ;Montez DF;Kay BP;Hatoum AS;Donohue MR;Foran W;Miller RL;Hendrickson TJ;Malone SM;Kandala S;Feczko E;Miranda-Dominguez O;Graham AM;Earl EA;Perrone AJ;Cordova M;Doyle O;Moore LA;Conan GM;Uriarte J;Snider K;Lynch BJ;Wilgenbusch JC;Pengo T;Tam A;Chen J;Newbold DJ;Zheng A;Seider NA;Van AN;Metoki A;Chauvin RJ;Laumann TO;Greene DJ;Petersen SE;Garavan H;Thompson WK;Nichols TE;Yeo BTT;Barch DM;Luna B;Fair DA;Dosenbach NUF
通讯作者：
Dosenbach NUF