Harnessing the frontal aslant tract's structure to assess its involvement in cognitive functions: new insights from 7-T diffusion imaging.
Cognitive processing speed
Episodic memory
Fluid intelligence
Frontal aslant tract
Sustained attention
Visuospatial orientation
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
29 Jul 2024
29 Jul 2024
Historique:
received:
28
01
2024
accepted:
08
07
2024
medline:
30
7
2024
pubmed:
30
7
2024
entrez:
29
7
2024
Statut:
epublish
Résumé
The first therapeutical goal followed by neurooncological surgeons dealing with prefrontal gliomas is attempting supramarginal tumor resection preserving relevant neurological function. Therefore, advanced knowledge of the frontal aslant tract (FAT) functional neuroanatomy in high-order cognitive domains beyond language and speech processing would help refine neurosurgeries, predicting possible relevant cognitive adverse events and maximizing the surgical efficacy. To this aim we performed the recently developed correlational tractography analyses to evaluate the possible relationship between FAT's microstructural properties and cognitive functions in 27 healthy subjects having ultra-high-field (7-Tesla) diffusion MRI. We independently assessed FAT segments innervating the dorsolateral prefrontal cortices (dlPFC-FAT) and the supplementary motor area (SMA-FAT). FAT microstructural robustness, measured by the tract's quantitative anisotropy (QA), was associated with a better performance in episodic memory, visuospatial orientation, cognitive processing speed and fluid intelligence but not sustained selective attention tests. Overall, the percentual tract volume showing an association between QA-index and improved cognitive scores (pQACV) was higher in the SMA-FAT compared to the dlPFC-FAT segment. This effect was right-lateralized for verbal episodic memory and fluid intelligence and bilateralized for visuospatial orientation and cognitive processing speed. Our results provide novel evidence for a functional specialization of the FAT beyond the known in language and speech processing, particularly its involvement in several higher-order cognitive domains. In light of these findings, further research should be encouraged to focus on neurocognitive deficits and their impact on patient outcomes after FAT damage, especially in the context of glioma surgery.
Identifiants
pubmed: 39075100
doi: 10.1038/s41598-024-67013-w
pii: 10.1038/s41598-024-67013-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17455Informations de copyright
© 2024. The Author(s).
Références
La Corte, E. et al. The frontal aslant tract: A systematic review for neurosurgical applications. Front. Neurol. https://doi.org/10.3389/fneur.2021.641586 (2021).
doi: 10.3389/fneur.2021.641586
pubmed: 33732210
pmcid: 7959833
Szmuda, T. et al. Frontal aslant tract projections to the inferior frontal gyrus. Folia Morphol. 76, 574–581 (2017).
doi: 10.5603/FM.a2017.0039
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
pubmed: 27437579
pmcid: 4990127
doi: 10.1038/nature18933
Briggs, R. G. et al. A connectomic atlas of the human cerebrum—Chapter 14: Tractographic description of the frontal aslant tract. Oper. Neurosurg. 15, S444 (2018).
doi: 10.1093/ons/opy268
Ruan, J. et al. Cytoarchitecture, probability maps, and functions of the human supplementary and pre-supplementary motor areas. Brain Struct. Funct. 223, 4169–4186 (2018).
pubmed: 30187192
pmcid: 6267244
doi: 10.1007/s00429-018-1738-6
Varriano, F., Pascual-Diaz, S. & Prats-Galino, A. When the FAT goes wide: Right extended frontal aslant tract volume predicts performance on working memory tasks in healthy humans. PLoS ONE 13, e0200786 (2018).
pubmed: 30067818
pmcid: 6070228
doi: 10.1371/journal.pone.0200786
Catani, M. et al. Short frontal lobe connections of the human brain. Cortex 48, 273–291 (2012).
pubmed: 22209688
doi: 10.1016/j.cortex.2011.12.001
de Schotten, M. T., Dell’Acqua, F., Valabregue, R. & Catani, M. Monkey to human comparative anatomy of the frontal lobe association tracts. Cortex 48, 82–96 (2012).
doi: 10.1016/j.cortex.2011.10.001
Ille, S., Engel, L., Kelm, A., Meyer, B. & Krieg, S. M. Language-eloquent white matter pathway tractography and the course of language function in glioma patients. Front. Oncol. 8, 572 (2018).
pubmed: 30574455
pmcid: 6291459
doi: 10.3389/fonc.2018.00572
Kinoshita, M. et al. Role of fronto-striatal tract and frontal aslant tract in movement and speech: An axonal mapping study. Brain Struct. Funct. 220, 3399–3412 (2015).
pubmed: 25086832
doi: 10.1007/s00429-014-0863-0
Sierpowska, J. et al. Morphological derivation overflow as a result of disruption of the left frontal aslant white matter tract. Brain Lang. 142, 54–64 (2015).
pubmed: 25658634
doi: 10.1016/j.bandl.2015.01.005
Blecher, T., Miron, S., Schneider, G. G., Achiron, A. & Ben-Shachar, M. Association between white matter microstructure and verbal fluency in patients with multiple sclerosis. Front. Psychol. 10, 1607 (2019).
pubmed: 31379663
pmcid: 6657651
doi: 10.3389/fpsyg.2019.01607
Keser, Z., Hillis, A. E., Schulz, P. E., Hasan, K. M. & Nelson, F. M. Frontal aslant tracts as correlates of lexical retrieval in MS. Neurol. Res. 42, 805–810 (2020).
pubmed: 32552566
pmcid: 7429310
doi: 10.1080/01616412.2020.1781454
Faulkner, J. W. & Wilshire, C. E. Mapping eloquent cortex: A voxel-based lesion-symptom mapping study of core speech production capacities in brain tumour patients. Brain Lang. 200, 104710 (2020).
pubmed: 31739187
doi: 10.1016/j.bandl.2019.104710
Dick, A. S., Garic, D., Graziano, P. & Tremblay, P. The frontal aslant tract (FAT) and its role in speech, language and executive function. Cortex 111, 148–163. https://doi.org/10.1016/j.cortex.2018.10.015 (2019).
doi: 10.1016/j.cortex.2018.10.015
pubmed: 30481666
Budisavljevic, S. et al. The role of the frontal aslant tract and premotor connections in visually guided hand movements. Neuroimage 146, 419–428 (2017).
pubmed: 27829166
doi: 10.1016/j.neuroimage.2016.10.051
Courtney, S. M., Petit, L., Haxby, J. V. & Ungerleider, L. G. The Role of Prefrontal Cortex in Working Memory: Examining the Contents of Consciousness.
Geula, C. et al. Frontal structural neural correlates of working memory performance in older adults. Front. Aging Neurosci. https://doi.org/10.3389/fnagi.2016.00328 (2017).
doi: 10.3389/fnagi.2016.00328
Thompson-Schill, S. L. et al. Effects of frontal lobe damage on interference effects in working memory. Cogn. Affect. Behav. Neurosci. 2, 109–120 (2002).
pubmed: 12455679
doi: 10.3758/CABN.2.2.109
Chai, W. J., Abd Hamid, A. I. & Abdullah, J. M. Working memory from the psychological and neurosciences perspectives: A review. Front. Psychol. 9, 401 (2018).
pubmed: 29636715
pmcid: 5881171
doi: 10.3389/fpsyg.2018.00401
Rizio, A. A. & Diaz, M. T. Language, aging, and cognition: Frontal aslant tract and superior longitudinal fasciculus contribute to working memory performance in older adults. Neuroreport 27, 689 (2016).
pubmed: 27138951
pmcid: 4955947
doi: 10.1097/WNR.0000000000000597
Motomura, K. et al. Supratotal resection of diffuse frontal lower grade gliomas with awake brain mapping, preserving motor, language, and neurocognitive functions. World Neurosurg. 119, 30–39 (2018).
pubmed: 30075269
doi: 10.1016/j.wneu.2018.07.193
Motomura, K. et al. Neurocognitive and functional outcomes in patients with diffuse frontal lower-grade gliomas undergoing intraoperative awake brain mapping. J. Neurosurg. 132, 1683–1691 (2019).
pubmed: 31100731
doi: 10.3171/2019.3.JNS19211
Dickerson, B. C. & Eichenbaum, H. The episodic memory system: Neurocircuitry and disorders. Neuropsychopharmacology 35, 86–104 (2010).
pubmed: 19776728
doi: 10.1038/npp.2009.126
Squire, L. R. & Zola, S. M. Episodic memory, semantic memory, and amnesia. Hippocampus 8, 205–211 (1998).
pubmed: 9662135
doi: 10.1002/(SICI)1098-1063(1998)8:3<205::AID-HIPO3>3.0.CO;2-I
Eichenbaum, H., Sauvage, M., Fortin, N., Komorowski, R. & Lipton, P. Towards a functional organization of episodic memory in the medial temporal lobe. Neurosci. Biobehav. Rev. 36, 1597–1608 (2012).
pubmed: 21810443
doi: 10.1016/j.neubiorev.2011.07.006
Mayes, A. R. & Roberts, N. Theories of episodic memory. Philos. Trans. R. Soc. Lond. B 356, 1395–1408 (2001).
doi: 10.1098/rstb.2001.0941
Pauli, E., Hildebrandt, M., Romstöck, J., Stefan, H. & Blümcke, I. Deficient memory acquisition in temporal lobe epilepsy is predicted by hippocampal granule cell loss. Neurology 67, 1383–1389 (2006).
pubmed: 17060564
doi: 10.1212/01.wnl.0000239828.36651.73
Allan, K., Dolan, R. J., Fletcher, P. C. & Rugg, M. D. The role of the right anterior prefrontal cortex in episodic retrieval. Neuroimage 11, 217–227 (2000).
pubmed: 10694464
doi: 10.1006/nimg.2000.0531
Gagnon, S. A. & Wagner, A. D. Acute stress and episodic memory retrieval: Neurobiological mechanisms and behavioral consequences. Ann. N. Y. Acad. Sci. 1369, 55–75 (2016).
pubmed: 26799371
doi: 10.1111/nyas.12996
Henson, R. N. A., Shallice, T. & Dolan, R. J. Right prefrontal cortex and episodic memory retrieval: A functional MRI test of the monitoring hypothesis. Brain 122, 1367–1381 (1999).
pubmed: 10388802
doi: 10.1093/brain/122.7.1367
Nyberg, L. et al. Large scale neurocognitive networks underlying episodic memory. J. Cogn. Neurosci. 12(1), 163–173 (2000).
pubmed: 10769313
doi: 10.1162/089892900561805
Andrés, P., Van der Linden, M. & Parmentier, F. B. R. Directed forgetting in frontal patients’ episodic recall. Neuropsychologia 45, 1355–1362 (2007).
pubmed: 17052735
doi: 10.1016/j.neuropsychologia.2006.09.012
Fang, S., Wang, Y. & Jiang, T. The influence of frontal lobe tumors and surgical treatment on advanced cognitive functions. World Neurosurg. 91, 340–346 (2016).
pubmed: 27072331
doi: 10.1016/j.wneu.2016.04.006
Serra, L. et al. Damage to the frontal aslant tract accounts for visuo-constructive deficits in Alzheimer’s disease. J. Alzheimer’s Dis. 60, 1015–1024 (2017).
doi: 10.3233/JAD-170638
Tsai, T.-H. et al. White matter microstructural alterations in amblyopic adults revealed by diffusion spectrum imaging with systematic tract-based automatic analysis. Br. J. Ophthalmol. 103, 511–516 (2019).
pubmed: 29844086
doi: 10.1136/bjophthalmol-2017-311733
Knowles, E. E. M. et al. The puzzle of processing speed, memory, and executive function impairments in schizophrenia: Fitting the pieces together. Biol. Psychiatry 78, 786–793 (2015).
pubmed: 25863361
pmcid: 4547909
doi: 10.1016/j.biopsych.2015.01.018
Glasser, M. F. et al. The minimal preprocessing pipelines for the human connectome project. Neuroimage 80, 105–124 (2013).
pubmed: 23668970
doi: 10.1016/j.neuroimage.2013.04.127
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
pubmed: 12377157
doi: 10.1006/nimg.2002.1132
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. Fsl. Neuroimage 62, 782–790 (2012).
pubmed: 21979382
doi: 10.1016/j.neuroimage.2011.09.015
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).
pubmed: 22248573
doi: 10.1016/j.neuroimage.2012.01.021
Andersson, J. L. R. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125, 1063–1078 (2016).
pubmed: 26481672
doi: 10.1016/j.neuroimage.2015.10.019
Andersson, J. L. R. & Sotiropoulos, S. N. Non-parametric representation and prediction of single-and multi-shell diffusion-weighted MRI data using Gaussian processes. Neuroimage 122, 166–176 (2015).
pubmed: 26236030
doi: 10.1016/j.neuroimage.2015.07.067
Andersson, J. L. R., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging. Neuroimage 20, 870–888 (2003).
pubmed: 14568458
doi: 10.1016/S1053-8119(03)00336-7
Gur, R. C. et al. A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: Standardization and initial construct validation. J. Neurosci. Methods 187, 254–262 (2010).
pubmed: 19945485
doi: 10.1016/j.jneumeth.2009.11.017
Gur, R. C. et al. Computerized neurocognitive scanning: I. Methodology and validation in healthy people. Neuropsychopharmacology 25, 766–776 (2001).
pubmed: 11682260
doi: 10.1016/S0893-133X(01)00278-0
Moore, T. M. et al. Development of an abbreviated form of the Penn line orientation test using large samples and computerized adaptive test simulation. Psychol. Assess. 27, 955 (2015).
pubmed: 25822834
pmcid: 4549167
doi: 10.1037/pas0000102
Bilker, W. B. et al. Development of abbreviated nine-item forms of the Raven’s standard progressive matrices test. Assessment 19, 354–369 (2012).
pubmed: 22605785
pmcid: 4410094
doi: 10.1177/1073191112446655
Weintraub, S. et al. Cognition assessment using the NIH toolbox. Neurology 80, S54–S64 (2013).
pubmed: 23479546
pmcid: 3662346
doi: 10.1212/WNL.0b013e3182872ded
Yeh, F.-C. et al. Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage 178, 57–68 (2018).
pubmed: 29758339
doi: 10.1016/j.neuroimage.2018.05.027
Yeh, F., Liu, L., Hitchens, T. K. & Wu, Y. L. Mapping immune cell infiltration using restricted diffusion MRI. Magn. Reson. Med. 77, 603–612 (2017).
pubmed: 26843524
doi: 10.1002/mrm.26143
Dadario, N. B., Tanglay, O. & Sughrue, M. E. Deconvoluting human Brodmann area 8 based on its unique structural and functional connectivity. Front. Neuroanat. 17, 1127143 (2023).
pubmed: 37426900
pmcid: 10323427
doi: 10.3389/fnana.2023.1127143
Kim, J. H. et al. Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: Functional connectivity-based parcellation method. Neuroimage 49, 2375–2386 (2010).
pubmed: 19837176
doi: 10.1016/j.neuroimage.2009.10.016
Panikratova, Y. R. et al. Functional connectivity of the dorsolateral prefrontal cortex contributes to different components of executive functions. Int. J. Psychophysiol. 151, 70–79 (2020).
pubmed: 32109499
doi: 10.1016/j.ijpsycho.2020.02.013
Narayana, S. et al. Electrophysiological and functional connectivity of the human supplementary motor area. Neuroimage 62, 250–265 (2012).
pubmed: 22569543
doi: 10.1016/j.neuroimage.2012.04.060
Hertrich, I., Dietrich, S., Blum, C. & Ackermann, H. The role of the dorsolateral prefrontal cortex for speech and language processing. Front. Hum. Neurosci. 15, 645209 (2021).
pubmed: 34079444
pmcid: 8165195
doi: 10.3389/fnhum.2021.645209
Yeh, F.-C. Population-based tract-to-region connectome of the human brain and its hierarchical topology. Nat. Commun. 13, 4933 (2022).
pubmed: 35995773
pmcid: 9395399
doi: 10.1038/s41467-022-32595-4
Yeh, F.-C. Shape analysis of the human association pathways. Neuroimage 223, 117329 (2020).
pubmed: 32882375
doi: 10.1016/j.neuroimage.2020.117329
Yeh, F.-C., Verstynen, T. D., Wang, Y., Fernández-Miranda, J. C. & Tseng, W.-Y.I. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE 8, e80713 (2013).
pubmed: 24348913
pmcid: 3858183
doi: 10.1371/journal.pone.0080713
Yeh, F.-C. et al. Automatic removal of false connections in diffusion MRI tractography using topology-informed pruning (TIP). Neurotherapeutics 16, 52–58 (2019).
pubmed: 30218214
doi: 10.1007/s13311-018-0663-y
Xing, Y. et al. White matter fractional anisotropy is a superior predictor for cognitive impairment than brain volumes in older adults with confluent white matter hyperintensities. Front. Psychiatry 12, 633811 (2021).
pubmed: 34025467
pmcid: 8131652
doi: 10.3389/fpsyt.2021.633811
Multani, N. et al. The association between white-matter tract abnormalities, and neuropsychiatric and cognitive symptoms in retired professional football players with multiple concussions. J. Neurol. 263, 1332–1341 (2016).
pubmed: 27142715
doi: 10.1007/s00415-016-8141-0
Palacios, E. M. et al. Diffusion tensor imaging differences relate to memory deficits in diffuse traumatic brain injury. BMC Neurol. 11, 1–11 (2011).
doi: 10.1186/1471-2377-11-24
Grieve, S. M., Williams, L. M., Paul, R. H., Clark, C. R. & Gordon, E. Cognitive aging, executive function, and fractional anisotropy: A diffusion tensor MR imaging study. Am. J. Neuroradiol. 28, 226–235 (2007).
pubmed: 17296985
pmcid: 7977408
Ezzati, A., Katz, M. J., Lipton, M. L., Zimmerman, M. E. & Lipton, R. B. Hippocampal volume and cingulum bundle fractional anisotropy are independently associated with verbal memory in older adults. Brain Imaging Behav. 10, 652–659 (2016).
pubmed: 26424564
pmcid: 4816657
doi: 10.1007/s11682-015-9452-y
Yeh, F.-C., Badre, D. & Verstynen, T. Connectometry: A statistical approach harnessing the analytical potential of the local connectome. Neuroimage 125, 162–171 (2016).
pubmed: 26499808
doi: 10.1016/j.neuroimage.2015.10.053
Yeh, F.-C. et al. Quantifying differences and similarities in whole-brain white matter architecture using local connectome fingerprints. PLoS Comput. Biol. 12, e1005203 (2016).
pubmed: 27846212
pmcid: 5112901
doi: 10.1371/journal.pcbi.1005203
Bukkieva, T. et al. Microstructural properties of brain white matter tracts in breast cancer survivors: A diffusion tensor imaging study. Pathophysiology 29, 595–609 (2022).
pubmed: 36278563
pmcid: 9624319
doi: 10.3390/pathophysiology29040046
IsaacsId, B. R. et al. 3 versus 7 Tesla magnetic resonance imaging for parcellations of subcortical brain structures in clinical settings. PLoS ONE https://doi.org/10.1371/journal.pone.0236208 (2020).
doi: 10.1371/journal.pone.0236208
Moon, H. C. et al. 7.0 Tesla MRI tractography in patients with trigeminal neuralgia. Magn. Reson. Imaging 54, 265–270 (2018).
pubmed: 29305127
doi: 10.1016/j.mri.2017.12.033
Lee, J. K. et al. 7T MRI versus 3T MRI of the brain in professional fighters and patients with head trauma. Neurotrauma Rep. 4, 342–349 (2023).
pubmed: 37284698
pmcid: 10240322
doi: 10.1089/neur.2023.0001
Gonzalez-Escamilla, G. & Groppa, S. 7 tesla MRI will soon be helpful to guide clinical practice in multiple sclerosis centres: no. Multipl. Scler. J. 27, 362–363 (2021).
doi: 10.1177/1352458520969662
Polders, D. L. et al. Signal to noise ratio and uncertainty in diffusion tensor imaging at 1.5, 3.0, and 7.0 tesla. J. Magn. Reson. Imaging 33, 1456–1463 (2011).
pubmed: 21591016
doi: 10.1002/jmri.22554
Melzer, T. R. et al. Test-retest reliability and sample size estimates after MRI scanner relocation. Neuroimage 211, 116608 (2020).
pubmed: 32032737
doi: 10.1016/j.neuroimage.2020.116608
Gilbert, S. J. et al. Functional specialization within rostral prefrontal cortex (area 10): A meta-analysis. J. Cogn. Neurosci. 18, 932–948 (2006).
pubmed: 16839301
doi: 10.1162/jocn.2006.18.6.932
Sousa, N., Cammarota, M., Cheng, S. & Numan, R. A prefrontal-hippocampal comparator for goal-directed behavior: The intentional self and episodic memory. Front. Behav. Neurosci. 9, 323 (2015).
Tsujimoto, S., Genovesio, A. & Wise, S. P. Frontal pole cortex: Encoding ends at the end of the endbrain. Trends Cogn. Sci. 15, 169–176 (2011).
pubmed: 21388858
doi: 10.1016/j.tics.2011.02.001
Squire, L. R., Genzel, L., Wixted, J. T. & Morris, R. G. Memory consolidation. Cold Spring Harb. Perspect. Biol. 7, 012788 (2015).
doi: 10.1101/cshperspect.a021766
Blouin, J., Pialasse, J. P., Mouchnino, L. & Simoneau, M. On the dynamics of spatial updating. Front. Neurosci. 16, 78007 (2022).
doi: 10.3389/fnins.2022.780027
Bartolomeo, P., de Schotten, M. T. & Chica, A. B. Brain networks of visuospatial attention and their disruption in visual neglect. Front. Hum. Neurosci. https://doi.org/10.3389/fnhum.2012.00110 (2010).
doi: 10.3389/fnhum.2012.00110
De Schotten, M. T. et al. A lateralized brain network for visuospatial attention. Nat. Neurosci. 14, 1245–1246 (2011).
doi: 10.1038/nn.2905
Eckert, M. A., Keren, N. I., Roberts, D. R., Calhoun, V. D. & Harris, K. C. Age-related changes in processing speed: Unique contributions of cerebellar and prefrontal cortex. Front. Hum. Neurosci. 4, 1178 (2010).
Kochunov, P. et al. Processing speed is correlated with cerebral health markers in the frontal lobes as quantified by neuroimaging. Neuroimage 49, 1190–1199 (2010).
pubmed: 19796691
doi: 10.1016/j.neuroimage.2009.09.052
Kennedy, K. M. & Raz, N. Aging white matter and cognition: Differential effects of regional variations in diffusion properties on memory, executive functions, and speed. Neuropsychologia 47, 916–927 (2009).
pubmed: 19166865
pmcid: 2643310
doi: 10.1016/j.neuropsychologia.2009.01.001
Drew, M. A., Starkey, N. J. & Isler, R. B. Examining the link between information processing speed and executive functioning in multiple sclerosis. Arch. Clin. Neuropsychol. 24, 47–58 (2009).
pubmed: 19395356
doi: 10.1093/arclin/acp007
Brown, L. A., Brockmole, J. R., Gow, A. J. & Deary, I. J. Processing speed and visuospatial executive function predict visual working memory ability in older adults. Exp. Aging Res. 38, 1–19 (2012).
pubmed: 22224947
doi: 10.1080/0361073X.2012.636722
Frischkorn, G. T., Schubert, A. L. & Hagemann, D. Processing speed, working memory, and executive functions: Independent or inter-related predictors of general intelligence. Intelligence 75, 95–110 (2019).
doi: 10.1016/j.intell.2019.05.003
Elgamal, S. A., Roy, E. A. & Sharratt, M. T. Age and verbal fluency: The mediating effect of speed of processing. Can. Geriatr. J. 14, 66–72 (2011).
pubmed: 23251316
pmcid: 3516352
doi: 10.5770/cgj.v14i3.17
Tagliaferri, M., Giampiccolo, D., Parmigiani, S., Avesani, P. & Cattaneo, L. Connectivity by the frontal aslant tract (FAT) explains local functional specialization of the superior and inferior frontal gyri in humans when choosing predictive over reactive strategies: A tractography-guided TMS study. J. Neurosci. 43, 6920–6929 (2023).
pubmed: 37657931
pmcid: 10573747
doi: 10.1523/JNEUROSCI.0406-23.2023
Chen, P. Y., Chen, C. L., Hsu, Y. C. & Tseng, W. Y. I. Fluid intelligence is associated with cortical volume and white matter tract integrity within multiple-demand system across adult lifespan. Neuroimage 212, 116576 (2020).
pubmed: 32105883
doi: 10.1016/j.neuroimage.2020.116576
Fry, A. F. & Hale, S. Processing speed, working memory, and fluid intelligence: Evidence for a developmental cascade. Psychol. Sci. 7, 237–241 (1996).
doi: 10.1111/j.1467-9280.1996.tb00366.x
Fry, A. F. & Hale, S. Relationships among processing speed, working memory, and fluid intelligence in children. Biol. Psychol. 54, 1–34 (2000).
pubmed: 11035218
doi: 10.1016/S0301-0511(00)00051-X
Conway, A. R. A., Cowan, N., Bunting, M. F., Therriault, D. J. & Minkoff, S. R. B. A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, and general fluid intelligence. Intelligence 30, 163–183 (2002).
doi: 10.1016/S0160-2896(01)00096-4
Cañas, A., Juncadella, M., Lau, R., Gabarrós, A. & Hernández, M. Working memory deficits after lesions involving the supplementary motor area. Front. Psychol. 9, 765 (2018).
pubmed: 29875717
pmcid: 5974158
doi: 10.3389/fpsyg.2018.00765
Sjöberg, R. L., Stålnacke, M., Andersson, M. & Eriksson, J. The supplementary motor area syndrome and cognitive control. Neuropsychologia 129, 141–145 (2019).
pubmed: 30930302
doi: 10.1016/j.neuropsychologia.2019.03.013
Brooks, M. Bridging Metacognition and Executive Function: Enhancing Metacognition via Development of the Dorsolateral Prefrontal Cortex (Springer, 2022).
Kroger, J. K. et al. Recruitment of anterior dorsolateral prefrontal cortex in human reasoning: A parametric study of relational complexity. Cereb. Cortex 12, 477–485 (2002).
pubmed: 11950765
doi: 10.1093/cercor/12.5.477
Fleming, S. M. & Dolan, R. J. The neural basis of metacognitive ability. Philos. Trans. R. Soc. B 367, 1338–1349 (2012).
doi: 10.1098/rstb.2011.0417
Tassy, S. et al. Disrupting the right prefrontal cortex alters moral judgement. Soc. Cogn. Affect. Neurosci. 7, 282–288 (2012).
pubmed: 21515641
doi: 10.1093/scan/nsr008
Hall, J. et al. A common neural system mediating two different forms of social judgement. Psychol. Med. 40, 1183–1192 (2010).
pubmed: 19811702
doi: 10.1017/S0033291709991395
van den Bent, M. J. et al. Adjuvant and concurrent temozolomide for 1p/19q non-co-deleted anaplastic glioma (CATNON; EORTC study 26053–22054): Second interim analysis of a randomised, open-label, phase 3 study. Lancet Oncol. 22, 813–823 (2021).
pubmed: 34000245
pmcid: 8191233
doi: 10.1016/S1470-2045(21)00090-5
Gritsch, S., Batchelor, T. T. & Gonzalez Castro, L. N. Diagnostic, therapeutic, and prognostic implications of the 2021 World Health Organization classification of tumors of the central nervous system. Cancer 128, 47–58 (2022).
pubmed: 34633681
doi: 10.1002/cncr.33918