Sources of Information Waste in Neuroimaging: Mishandling Structures, Thinking Dichotomously, and Over-Reducing Data Article Swipe
Gang Chen
,
Paul A. Taylor
,
Joel Stoddard
,
Robert W. Cox
,
Peter A. Bandettini
,
Luiz Pessoa
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.52294/2e179dbf-5e37-4338-a639-9ceb92b055ea
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.52294/2e179dbf-5e37-4338-a639-9ceb92b055ea
Neuroimaging relies on separate statistical inferences at tens of thousands of spatial locations. Such massively univariate analysis typically requires an adjustment for multiple testing in an attempt to maintain the family-wise error rate at a nominal level of 5%. First, we examine three sources of substantial information loss that are associated with the common practice under the massively univariate framework: (a) the hierarchical data structures (spatial units and trials) are not well maintained in the modeling process; (b) the adjustment for multiple testing leads to an artificial step of strict thresholding; (c) information is excessively reduced during both modeling and result reporting. These sources of information loss have far-reaching impacts on result interpretability as well as reproducibility in neuroimaging. Second, to improve inference efficiency, predictive accuracy, and generalizability, we propose a Bayesian multilevel modeling framework that closely characterizes the data hierarchies across spatial units and experimental trials. Rather than analyzing the data in a way that first creates multiplicity and then resorts to a post hoc solution to address them, we suggest directly incorporating the cross-space information into one single model under the Bayesian framework (so there is no multiplicity issue). Third, regardless of the modeling framework one adopts, we make four actionable suggestions to alleviate information waste and to improve reproducibility: (1) model data hierarchies, (2) quantify effects, (3) abandon strict dichotomization, and (4) report full results. We provide examples for all of these points using both demo and real studies, including the recent Neuroimaging Analysis Replication and Prediction Study (NARPS).
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.52294/2e179dbf-5e37-4338-a639-9ceb92b055ea
- OA Status
- diamond
- Cited By
- 2
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220838865
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- OpenAlex ID
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https://openalex.org/W4220838865Canonical identifier for this work in OpenAlex
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https://doi.org/10.52294/2e179dbf-5e37-4338-a639-9ceb92b055eaDigital Object Identifier
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Sources of Information Waste in Neuroimaging: Mishandling Structures, Thinking Dichotomously, and Over-Reducing DataWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-01Full publication date if available
- Authors
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Gang Chen, Paul A. Taylor, Joel Stoddard, Robert W. Cox, Peter A. Bandettini, Luiz PessoaList of authors in order
- Landing page
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https://doi.org/10.52294/2e179dbf-5e37-4338-a639-9ceb92b055eaPublisher landing page
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diamondOpen access status per OpenAlex
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https://doi.org/10.52294/2e179dbf-5e37-4338-a639-9ceb92b055eaDirect OA link when available
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Computer science, Neuroimaging, Bayesian probability, Inference, Data mining, Univariate, Generalizability theory, Bayesian inference, Interpretability, Machine learning, Data science, Artificial intelligence, Statistics, Psychology, Mathematics, Multivariate statistics, PsychiatryTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.raw_source_name | Aperture Neuro |
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| primary_location.id | doi:10.52294/2e179dbf-5e37-4338-a639-9ceb92b055ea |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4387281425 |
| primary_location.source.issn | 2957-3963 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2957-3963 |
| primary_location.source.is_core | True |
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| primary_location.source.display_name | Aperture Neuro |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Aperture Neuro |
| primary_location.landing_page_url | https://doi.org/10.52294/2e179dbf-5e37-4338-a639-9ceb92b055ea |
| publication_date | 2022-03-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2479350029, https://openalex.org/W2051691068, https://openalex.org/W2044634376, https://openalex.org/W2046557060, https://openalex.org/W2134305330, https://openalex.org/W2169463832, https://openalex.org/W2986146075, https://openalex.org/W2944676855, https://openalex.org/W4248681815, https://openalex.org/W2924950986, https://openalex.org/W3139007579, https://openalex.org/W2119303750, https://openalex.org/W2018742491, https://openalex.org/W2293040502, https://openalex.org/W2167415089, https://openalex.org/W2923400604, https://openalex.org/W2609984900, https://openalex.org/W2154449833, https://openalex.org/W2029671471, https://openalex.org/W2952384350, https://openalex.org/W2149719430, https://openalex.org/W2105090664, https://openalex.org/W2785114431, https://openalex.org/W2893980603, https://openalex.org/W3210230948, https://openalex.org/W2951354032, https://openalex.org/W3011348916, https://openalex.org/W3021752304, https://openalex.org/W3041613674, https://openalex.org/W109386094, https://openalex.org/W2162888823, https://openalex.org/W1913823733, https://openalex.org/W2083599439, https://openalex.org/W4232976691, https://openalex.org/W2900101937, https://openalex.org/W2801251405, https://openalex.org/W2807243083, https://openalex.org/W2965053852, https://openalex.org/W2884558895, https://openalex.org/W1976994512, https://openalex.org/W2131481495, https://openalex.org/W1991237518, https://openalex.org/W2169005503, https://openalex.org/W2138266733, https://openalex.org/W2071415508, https://openalex.org/W3158267102, https://openalex.org/W3136237691, https://openalex.org/W2018102971, https://openalex.org/W3153109663, https://openalex.org/W2087720347, https://openalex.org/W2895108013, https://openalex.org/W2116581177, https://openalex.org/W2032946087, https://openalex.org/W2950086410, https://openalex.org/W3094438142, https://openalex.org/W2724821146, https://openalex.org/W3032707732, https://openalex.org/W3185289947, https://openalex.org/W2950996173, https://openalex.org/W1849956890, https://openalex.org/W2155624710 |
| referenced_works_count | 61 |
| abstract_inverted_index.a | 34, 130, 153, 163 |
| abstract_inverted_index.We | 228 |
| abstract_inverted_index.an | 19, 25, 85 |
| abstract_inverted_index.as | 113, 115 |
| abstract_inverted_index.at | 6, 33 |
| abstract_inverted_index.in | 24, 73, 117, 152 |
| abstract_inverted_index.is | 93, 187 |
| abstract_inverted_index.no | 188 |
| abstract_inverted_index.of | 8, 10, 37, 44, 88, 104, 193, 233 |
| abstract_inverted_index.on | 2, 110 |
| abstract_inverted_index.to | 27, 84, 120, 162, 167, 204, 209 |
| abstract_inverted_index.we | 40, 128, 170, 199 |
| abstract_inverted_index.(1) | 212 |
| abstract_inverted_index.(2) | 216 |
| abstract_inverted_index.(3) | 219 |
| abstract_inverted_index.(4) | 224 |
| abstract_inverted_index.(a) | 60 |
| abstract_inverted_index.(b) | 77 |
| abstract_inverted_index.(c) | 91 |
| abstract_inverted_index.(so | 185 |
| abstract_inverted_index.5%. | 38 |
| abstract_inverted_index.all | 232 |
| abstract_inverted_index.and | 67, 99, 126, 144, 159, 208, 223, 239, 248 |
| abstract_inverted_index.are | 49, 69 |
| abstract_inverted_index.for | 21, 80, 231 |
| abstract_inverted_index.hoc | 165 |
| abstract_inverted_index.not | 70 |
| abstract_inverted_index.one | 178, 197 |
| abstract_inverted_index.the | 29, 52, 56, 61, 74, 78, 138, 150, 174, 182, 194, 243 |
| abstract_inverted_index.way | 154 |
| abstract_inverted_index.Such | 13 |
| abstract_inverted_index.both | 97, 237 |
| abstract_inverted_index.data | 63, 139, 151, 214 |
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| abstract_inverted_index.full | 226 |
| abstract_inverted_index.have | 107 |
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| abstract_inverted_index.tens | 7 |
| abstract_inverted_index.than | 148 |
| abstract_inverted_index.that | 48, 135, 155 |
| abstract_inverted_index.then | 160 |
| abstract_inverted_index.well | 71, 114 |
| abstract_inverted_index.with | 51 |
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| abstract_inverted_index.These | 102 |
| abstract_inverted_index.error | 31 |
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| abstract_inverted_index.leads | 83 |
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| abstract_inverted_index.model | 180, 213 |
| abstract_inverted_index.them, | 169 |
| abstract_inverted_index.there | 186 |
| abstract_inverted_index.these | 234 |
| abstract_inverted_index.three | 42 |
| abstract_inverted_index.under | 55, 181 |
| abstract_inverted_index.units | 66, 143 |
| abstract_inverted_index.using | 236 |
| abstract_inverted_index.waste | 207 |
| abstract_inverted_index.First, | 39 |
| abstract_inverted_index.Rather | 147 |
| abstract_inverted_index.Third, | 191 |
| abstract_inverted_index.across | 141 |
| abstract_inverted_index.common | 53 |
| abstract_inverted_index.during | 96 |
| abstract_inverted_index.points | 235 |
| abstract_inverted_index.recent | 244 |
| abstract_inverted_index.relies | 1 |
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| abstract_inverted_index.strict | 89, 221 |
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| abstract_inverted_index.abandon | 220 |
| abstract_inverted_index.address | 168 |
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| abstract_inverted_index.creates | 157 |
| abstract_inverted_index.examine | 41 |
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| abstract_inverted_index.issue). | 190 |
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| abstract_inverted_index.provide | 229 |
| abstract_inverted_index.reduced | 95 |
| abstract_inverted_index.resorts | 161 |
| abstract_inverted_index.sources | 43, 103 |
| abstract_inverted_index.spatial | 11, 142 |
| abstract_inverted_index.suggest | 171 |
| abstract_inverted_index.testing | 23, 82 |
| abstract_inverted_index.trials) | 68 |
| abstract_inverted_index.trials. | 146 |
| abstract_inverted_index.(NARPS). | 251 |
| abstract_inverted_index.(spatial | 65 |
| abstract_inverted_index.Analysis | 246 |
| abstract_inverted_index.Bayesian | 131, 183 |
| abstract_inverted_index.analysis | 16 |
| abstract_inverted_index.directly | 172 |
| abstract_inverted_index.effects, | 218 |
| abstract_inverted_index.examples | 230 |
| abstract_inverted_index.maintain | 28 |
| abstract_inverted_index.modeling | 75, 98, 133, 195 |
| abstract_inverted_index.multiple | 22, 81 |
| abstract_inverted_index.practice | 54 |
| abstract_inverted_index.process; | 76 |
| abstract_inverted_index.quantify | 217 |
| abstract_inverted_index.requires | 18 |
| abstract_inverted_index.results. | 227 |
| abstract_inverted_index.separate | 3 |
| abstract_inverted_index.solution | 166 |
| abstract_inverted_index.studies, | 241 |
| abstract_inverted_index.accuracy, | 125 |
| abstract_inverted_index.alleviate | 205 |
| abstract_inverted_index.analyzing | 149 |
| abstract_inverted_index.framework | 134, 184, 196 |
| abstract_inverted_index.including | 242 |
| abstract_inverted_index.inference | 122 |
| abstract_inverted_index.massively | 14, 57 |
| abstract_inverted_index.thousands | 9 |
| abstract_inverted_index.typically | 17 |
| abstract_inverted_index.Prediction | 249 |
| abstract_inverted_index.actionable | 202 |
| abstract_inverted_index.adjustment | 20, 79 |
| abstract_inverted_index.artificial | 86 |
| abstract_inverted_index.associated | 50 |
| abstract_inverted_index.framework: | 59 |
| abstract_inverted_index.inferences | 5 |
| abstract_inverted_index.locations. | 12 |
| abstract_inverted_index.maintained | 72 |
| abstract_inverted_index.multilevel | 132 |
| abstract_inverted_index.predictive | 124 |
| abstract_inverted_index.regardless | 192 |
| abstract_inverted_index.reporting. | 101 |
| abstract_inverted_index.structures | 64 |
| abstract_inverted_index.univariate | 15, 58 |
| abstract_inverted_index.Replication | 247 |
| abstract_inverted_index.cross-space | 175 |
| abstract_inverted_index.efficiency, | 123 |
| abstract_inverted_index.excessively | 94 |
| abstract_inverted_index.family-wise | 30 |
| abstract_inverted_index.hierarchies | 140 |
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| abstract_inverted_index.statistical | 4 |
| abstract_inverted_index.substantial | 45 |
| abstract_inverted_index.suggestions | 203 |
| abstract_inverted_index.Neuroimaging | 0, 245 |
| abstract_inverted_index.experimental | 145 |
| abstract_inverted_index.far-reaching | 108 |
| abstract_inverted_index.hierarchical | 62 |
| abstract_inverted_index.hierarchies, | 215 |
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| abstract_inverted_index.characterizes | 137 |
| abstract_inverted_index.incorporating | 173 |
| abstract_inverted_index.neuroimaging. | 118 |
| abstract_inverted_index.thresholding; | 90 |
| abstract_inverted_index.reproducibility | 116 |
| abstract_inverted_index.dichotomization, | 222 |
| abstract_inverted_index.interpretability | 112 |
| abstract_inverted_index.reproducibility: | 211 |
| abstract_inverted_index.generalizability, | 127 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.64756989 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |