A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | AA | AB | AC | AD | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Year | DOI | Article Title | Study Sample | Data Source | Data Source Details | Software | Analysis Details | PMID | |||||||||||||||||||||
2 | PMID: 23832422 PMCID: PMC3840110 | 2013 | 10.1007/s00213-013-3198-2 | A longitudinal examination of adolescent response inhibition: neural differences before and after the initiation of heavy drinking | Adolescent heavy drinkers scanned at baseline and one-year follow up | Private | AFNI | A repeated measures ANCOVA (AFNI 3dANOVA3) with time (baseline and follow-up) as the within-subjects factor and group (heavy drinker n = 20 versus control n = 20) as the between-subjects factor determines main effects of drinking group and time and their interaction. | 23832422 | |||||||||||||||||||||
3 | PMID: 22420593 PMCID: PMC3308134 | 2012 | 10.1111/j.1399-5618.2012.01002.x | A longitudinal functional connectivity analysis of the amygdala in bipolar I disorder across mood states | 15 bipolar patients and 15 HC | Private | AFNI | The individual activation maps of z-scores were combined across participants in a 2 · 2 analysis of variance (ANOVA) to produce whole-brain composite maps. The ANOVA was performed using the GroupAna tool set in AFNI with group (HC and bipolar I disorder subjects) and time (baseline and repeat scan) as the two factors. | 22420593 | |||||||||||||||||||||
4 | PMID: 23724030 PMCID: PMC3665895 | 2013 | 10.1371/journal.pone.0064154 | A longitudinal study of hand motor recovery after sub-acute stroke: A study combined fMRI with diffusion tensor imaging | 12 ischemic stroke patients from the Beijing Tiantan Hospital & 11 age- and sex-matched controls (no history of psychiatric or neurological disorder, also from the Beijing Tiantan Hospital) | Private | Matlab 7.6.0/SPM8 | Statistic analysis was performed in two steps. First, a singlesubject fixed effects model was used. The difference betweenthestimulation and the baseline was estimated at each voxel byusing the general linear model (GLM) and the parameter for thecovariate resulting from the least mean square fit of the model tothe data were estimated, then the statistical parameter maps of thet statistic resulting from a linear contrast of the covariate weregenerated for each subject. The statistical threshold was set atP,0.05(corrected for multiple comparisons). In second-levelanalysis, the obtained individual t-maps were used in `randomeffect' group analysis framework by one-sample t-test for differentgroups. In order to study the motor network reorganization, weinvestigated the longitudinal changes in task-related BOLDactivities in stroke patients using the pair-t test. Further, we alsoperformed the two-sample t-test to learn the changes of brainactivity between the patients and the healthy subjects. | 23724030 | |||||||||||||||||||||
5 | PMID: 25961712 PMCID: PMC4427400 | 2015 | 10.1371/journal.pone.0126326 | Accounting for dynamic fluctuations across time when examining fMRI test-retest reliability: Analysis of a reward paradigm in the EMBARC study | 40 HC and 40 MDD tested twice, once week apart | EMBARQ | https://ndar.nih.gov/data_from_labs.html under Study #2199 | SPSS | Data from the VS and occipital ROIs were analyzed using t-tests and repeated measures analysis of variance (ANOVA), to examine relevant effects of time, hemisphere, and in the case of VS, contrast type (RE, PE) on the activation within these regions using SPSS (IBM SPSS Statistics Version 20.0). | 25961712 | ||||||||||||||||||||
6 | PMID: 23689388 | 2013 | 10.1038/sc.2013.41 | Brain activation in the acute phase of traumatic spinal cord injury | n=6 tetraplegic adults, scanned at injury and one-year follow-up | Private | SPM8 | Modelling was done with boxcar functions convolved with canonical hemodynamic response function (HRF). Estimation of model parameters was done by using SPM8, after which task versus rest activation was assessed by applying a t-test to the parameter estimates, resulting in statistical parametric t-maps for each subject.Finally, weighted laterality index (wLI) was calculated by using the combined bootstrap/histogram analysis approach. | 23689388 | |||||||||||||||||||||
7 | PMID: 24718105 PMCID: PMC3981758 | 2014 | 10.1371/journal.pone.0093715 | Brain activity changes in cognitive networks in relapsing-remitting multiple sclerosis – Insights from a longitudinal fMRI study | n=15 MS patients at baseline and one-year follow-up | Private | SPSS | SPSS was used to test for inter- and intra-group differences for patients and HC, using the K-S Test for normal distribution, the U- and Wilcoxon-tests for non-parametrical comparisons, the (paired) t-Test for parametrical comparisons, and regression analysis to test for the predictive value of the SDMT on subcortical grey matter volumes. To further explore variability of the fMRI signal changes over time, to test for any systematic difference (i.e. fixed bias) between the measurements, and to identify possible outliers, Bland-Altman (BA) plots for specific region of interests (ROI) were generated for paired runs (FU-BL mean activation vs. rest) in HC and in patients. | 24718105 | |||||||||||||||||||||
8 | PMID: 24995850 | 2014 | 10.1111/pme.12460 | Changes in clinical pain in fibromyalgia patients correlate with changes in brain activation in the cingulate cortex in a response inhibition task | All study participants were taking part in a larger randomized controlled clinical treatment trial for FM. | Private | AFNI (AlphaSim) | 1. the calculation of differences in activation between correct No-Go and Go trials in each individual at time points 1 and 2 (= first level contrast images—2 per study subjects), 2. calculation of difference images between time points 1 and 2 (= 1 Δ image per FM patient), 3. correlation of Δ images with changes in clinical pain (Δclinical pain): for Δpain intensity (Analyses 1a) and Δ%BP (Analysis 1b), and 4. performance of repeated measures ANOVA with contrast images comparing over improvers and HC (Analysis 2a), and FM—improvers, and FM—nonimprovers (Analysis 2b). | 24995850 | |||||||||||||||||||||
9 | PMID: 25980483 PMCID: PMC4534487 | 2015 | 10.1007/s00213-015-3959-1 | Common and distinct neural effects of risperidone and olanzapine during procedural learning in schizophrenia: A randomised longitudinal fMRI study | 30 SZ patients scanned twice 7-8 weeks apart | Private | SPSS/SPM | Significant changes from baseline to follow-up in atypical antipsychotic group were examined with group (typical, atypical) × occasion (baseline, follow-up) SPM ANOVA, followed by paired t-tests separately in the typical and atypical groups. fMRI data were analysed using a two-stage random-effects procedure. The first stage identified subject-specific task-related activations relevant to pattern trials (experimental condition) over the entire session as well as linear increases and decreases in activity over the five blocks of pattern trials. Random trials (control condition) served as the implicit baseline. Second stage of analysis, the resulting maps were used to establish task related activations across subject-specific images using one-sample t-tests for each group (typical, atypical; risperidone, olanzapine) at baseline and follow-up. | 25980483 | |||||||||||||||||||||
10 | PMID: 12429600 | 2002 | 10.1093/brain/awf282 | Correlation between motor improvements and altered fMRI activity after rehabilitative therapy | 7 stroke patients scanned 4 times at two week intervals | Private | FSL/MEDx | Two pre-treatment and two post-treatment Z statistic images were summed across participants and divided by square root of number of participants. For recovery-weighted analysis, data across sessions were combined through fixed-effects comparison of two post- minus two pre-treatment sessions. Subject-specific "therapy-related increases" images were combined across the group by summing and dividing by the square root of number of participants. | 12429600 | |||||||||||||||||||||
11 | PMID: 25024729 PMCID: PMC4082872 | 2014 | 10.1155/2014/465760 | Differential Activation Patterns of fMRI in Sleep-Deprived Brain: Restoring Effects of Acupuncture | n=16 healthy volunteers, 3 fMRI sessions: one following normal sleep and acupuncture at SP6, and the other two after a night of total sleep deprivation with acupuncture on SP6 | Private | SPM8 | A one-way within-subject ANOVA and post were performed and paired t-test for group analysis was performed to compared regional brain activity with acupunture versus rest for rested wakeful and sleep depreivation | 25024729 | |||||||||||||||||||||
12 | PMID: 21502525 PMCID: PMC3088578 | 2011 | 10.1073/pnas.1018985108 | Dynamic reconfiguration of human brain networks during learning | Healthy Young Adults | Private | Matlab and Statistica | Graph theory | 21502525 | |||||||||||||||||||||
13 | PMID: 26061879 PMCID: PMC4465482 | 2015 | 10.1371/journal.pone.0128964 | Emotion reactivity is increased 4-6 Weeks postpartum in healthy women: A longitudinal fMRI study | 13 postpartum women scanned twice (48 hours after and 4-6 weeks after delivery). 15 women scanned twice (once in lueal, once in follicular stage). | Data Dryad | https://datadryad.org/resource/doi:10.5061/dryad.r1pf4 | SPSS/SPM | Differences between early and late postpartum test sessions and between women postpartum and healthy controls were analyzed with SPM using paired t-tests and an ANOVA followed by regular t-tests respectively. | 26061879 | ||||||||||||||||||||
14 | PMID: 22936899 PMCID: PMC3424489 | 2012 | 10.3389/fnana.2012.00032 | Experience-dependent plasticity in white matter microstructure: reasoning training alters structural connectivity | Pre-Law Students (23 trained, 22 controls) scanned two times | Private | TBSS, FSL (FDT) | We performed voxel-wise statistical analysis. Each subject’s aligned FA, AD, RD, and MD data were then projected onto this skeleton by finding the nearest maximum FA value for the individual. This projection step aims to remove the effect of cross-subject spatial variability that remains after the non-linear registration.Skeletonized difference images (time 2–time 1) were created for each subject, and the resulting data were fed into an unpaired t-test to compare the trained group to the control group. Voxel-wise cross-subject permutation-based nonparametric statistics were performed using Randomis. We tested for correlations between LSAT improvement, as measured by the difference between the first and fourth practice test, and diffusion changes at the whole brain level following the approach described above. We then tested for brain-behavior correlations in the anatomical regions defined by the Johns Hopkins University White Matter Label Atlas. we decided to perform an exploratory analysis because we considered that brain-behavior relationships might be most prominent in tracts less centrally involved in reasoning. Therefore, we tested all 48 labels and corrected all statistics for multiple comparisons using a randomization-based family-wise error correction | 22936899 | |||||||||||||||||||||
15 | PMID: 27176918 PMCID: PMC5003759 | 2016 | 10.3233/RNN-150621 | Imaging network level language recovery after left PCA stroke | 4 Stroke Patients | Private | FSL | Task Data: Longitudinal analyses were carried out to investigate whether or not the patterns of activation differed significantly between the acute scan and follow up time points for each participant. We contrasted 6 months activation to that of acute and subacute and between subacute and acute. Change in BOLD signal over time during phone-mic+word cued naming versus scrambled pictures and noise cued naming versus scrambled pictures were estimated. Resting State Data: Correlation matrices were generated for 12 ROIs in the language network, converted to Fisher z-scores. Repeated for all participants at all three time periods (no further statistical tests on those results). | 27176918 | |||||||||||||||||||||
16 | PMID: 23486950 PMCID: PMC3657728 | 2013 | 10.1523/JNEUROSCI.4141-12.2013 | Intensive reasoning training alters patterns of brain connectivity at rest | Pre-Law Students | Private | FSL | Pearson correlations for each pair of regions were computed and Fischer transformed to produce normally distributed values (z-scores). Paired t tests comparing time 1 to time 2 data were performed for the trained group. Group x Time ANOVAs were conducted to test whether changes in the trained group were greater than variability in the control group. Finally, unpaired t tests were conducted to exclude correlation pairs that differed between groups at time 1 from further analysis. | 23486950 | |||||||||||||||||||||
17 | PMID: 26937383 PMCID: PMC4753807 | 2016 | 10.1016/j.nicl.2016.01.016 | Language at rest: A longitudinal study of intrinsic functional connectivity in preterm children | n=13 preterm and n=2 term children, assessed at 8 years old and 16 years old | Private | AFNI | To compare the longitudinal changes of intrinsic connectivity across the groups, a Linear Mixed Effects model (LME) using age (8 years vs. 16 years ) and grou p (PT vs. T ) as facto rs was compu ted us in g AFNI . The age by group interaction was investigated using a threshold signif- icance of p b 0.05 with a conjoint cluster of 184 voxels corresponding to a p b 0.05 family-wise error (FWE) correction as determined by AFNI's AlphaSim program. | 26937383 | |||||||||||||||||||||
18 | PMID: 26648521 PMCID: PMC4682164 | 2015 | 10.1038/ncomms9885 | Long-term neural and physiological phenotyping of a single human | 1 healthy adult male | MyConnectome | http://myconnectome.org/wp/data-sharing | Custom code (python) | We also assessed the long-range temporal dynamics of connectivity across the entire connectome, by comparing each session to the mean across all sessions more complex mesoscale dynamics. Graph–theoretic network metrics were also computed using signed/weighted connectivity (correlation) matrices derived from the parcellated connectome, generating measures of functional segregation (modularity) and functional integration (global efficiency)10 for each session. We assessed the relation between task-based connectivity and resting connectivity using a meta-analytic connectivity modelling approach13; mean values for each parcel were extracted from statistical maps for each of the 237 statistical contrasts (across all sessions and tasks, except for retinotopy),and these were then used to generate a connectivity matrix reflecting the correlation of parcel activation across contrasts.Tractography was computed on diffusion data combined across 14 diffusion acquisitions, using the resting-state parcels as seed regions to obtain parcellated structural connectomes analogous to those computed for rsfMRI. Connectome similarity over time: correlations (python) | 26648521 | ||||||||||||||||||||
19 | PMID: 27069924 PMCID: PMC4812188 | 2016 | 10.1155/2016/7403795 | Longitudinal assessment of motor recovery of contralateral hand after basal ganglia infarction using functional magnetic resonance imaging | 4 patients with hemiplegia, scanned at different stages of acute or chronic motor disability | Private | FSL/SPM5 | Data from each patient were independently analyzed without resorting to standardization. After preprocessing, 𝑡-tests across pixels were performed to obtain statistical parametric maps. 𝑃 < 0.05 was considered statistically significant. The threshold of activated pixels was set as ≥5 pixels, indicating that regions with ≥5 continuously activated pixels were effectively considered as activated brain areas. Localization of activated pixels in specific brain regions was achieved using T1-weighted images, and BOLD-fMRI data were registered to the subject’s anatomical structures. The 𝑡-values from statistical analyses were used to reflect activation intensity, and the activation range was calculated by the summation of the number of pixels in each activated brain area. DTI data was analyzed with the FMRIB Software Library. NOTE: It doesn't look like there was an actual longitudinal analysis, just presentation of images at different time points. | 27069924 | |||||||||||||||||||||
20 | PMID: 23616547 PMCID: PMC3809090 | 2013 | 10.1523/JNEUROSCI.4074-12.2013 | Longitudinal change in the neural bases of adolescent social self-evaluations: Effects of age and pubertal development | 27 neurotypical individuals; scanned twice at ages 10 and 13 | Private | SPM8 | T1 and T2 were modeled as two separate runs. The resulting contrast images were entered into group-level analyses using random effects models. Paired t tests and a 2 2 2 ANOVA was conducted with three factors: target (self and other), domain (social and academic), and age (T1 and T2). Unless otherwise noted, results were thresholded at p 0.001, k 10 voxels (3 3 3 mm), to balance between types I and II errors; in particular, results surviving FWE correction are indicated as such in the text and table. | 23616547 | |||||||||||||||||||||
21 | PMID: 26269638 PMCID: PMC4532760 | 2015 | 10.1523/JNEUROSCI.1553-15.2015 | Longitudinal changes in prefrontal cortex activation underlie declines in adolescent risk taking | 22 adolescents; scanned two times ~1.5 yrs apart | Private | (GLM) in SPM8 /AFNI | Thus, to control for any potential changes in global brain activation over time, we modeled the first-level models by concatenating neural activation at T1 and T2, such that the implicit baseline is averaged across the two time points. In each participant’s fixed-effects analysis, a GLM was created with regressors of interest to separate different events: risk taking at T1 and T2. To correct for multiple comparisons, we conducted a Monte Carlo simulation. We used our group-level brain mask combined with the gray mask in SPM, therefore representing neural coverage in our sample that corresponded to gray matter. All statistical analyses reported are two-tailed. We used the MarsBaR toolbox within SPM to extract parameter estimates from significant clusters in the group-level analyses. Parameter estimates of signal intensity were extracted from the entire cluster of activation. | 26269638 | |||||||||||||||||||||
22 | PMID: 26929085 | 2016 | 10.1111/ejn.13222 | Longitudinal functional connectivity changes correlate with mood improvement after regular exercise in a dose-dependent fashion | 38 healthy participants; scanned two times, before and after exercise program | Private | SPM12/CONN toolbox | NBS (Zalesky et al., 2010) is a novel approach that seeks to identify differences between two datasets in any set of connected structures. In our case, we wanted to study the changes in the functional connections after our exercise programme of the right parahippocampal gyrus to all other AAL regions (nodes), by taking into account the correlation coefficient between their time series (edgevalue). The NBS procedure was ran as implemented in the CONN toolbox and comprised five steps: (i) a t-test between the two time points for the values of each edge independently; (ii) thresholding the t-statistic (P < 0.01 uncorrected) at each edge to form a set of suprathreshold edges; (iii) identifying the functionally connected components defined by the set of suprathreshold edges; (iv) computing the size of each component identified (number of edges) and its intensity (sum of absolute T-values) at the two time points; and (v) repeating steps (i)–(iv), each time randomly permuting members of the two populations and storing the size and intensity of the largest component identified for each permutation. This yields an empirical estimate of the null distribution of maximal component size and intensity. A corrected P-value for the difference between the two time points for each observed component was then calculated using this null distribution within each group and across the two groups, using post hoc t-tests in a general linear model accounting for age and gender (also implemented in CONN). The significance threshold was P < 0.05, false discovery rate (FDR)-corrected for multiple comparisons. | 26929085 | |||||||||||||||||||||
23 | PMID: 24227721 PMCID: PMC3828464 | 2013 | 10.1523/JNEUROSCI.1741-13.2013 | Longitudinal Growth Curves of Brain Function Underlying Inhibitory Control through Adolescence | n=123, 302 visits (9-26 years) | Private | HLM version 6 | Longitudinal growth curve modeling: Growth curve modeling extends multiple regres- sion for use with repeated-measures data and involves statistical model- building procedures to (1) model general patterns of developmental change, (2) test for significant individual differences in intercepts and slopes of individual growth models, and (3) test predictors of the inter- cept and the slope that may explain individual differences (Singer and Willett, 2003). Growth curve modeling was accomplished in the present study using HLM analyses (also termed “random effects,” “mixed ef- fects,” or “multilevel modeling”). HLM utilizes multilevel fixed and random-effects analyses to account for the nesting of data within indi- viduals. Further, HLM uniquely permits flexible modeling of time, so that data collected at uneven intervals and from individuals with varying numbers of time points can be included in the model (Bryk and Rauden- bush, 2002; Raudenbush and Bryk, 2002). | 24227721 | |||||||||||||||||||||
24 | PMID: 26163799 | 2016 | 10.1016/j.neuroimage.2015.07.010 | Longitudinal reproducibility of default-mode network connectivity in healthy elderly participants: A multicentric resting-state fMRI study | Each of 13 sites scanned 5 participants (65 total) twice median of two weeks apart. | Private | PharmaCog Consortium | SPM8 | Test-retest comparison of DMN functional connectivity extracted from seed-based analysis, site ICA, and consortium ICA were performed via Jaccard coefficient of maps thresholded at z > 2. Test-retest reproducibility was measured as absolute percent difference between DMN functional connectivity maps of test and retest sessions. | 26163799 | ||||||||||||||||||||
25 | PMID: 24071913 | 2014 | 10.1007/s00406-013-0447-7 | Longitudinal task-negative network analyses in preclinical Huntington’s disease | 26 participants (13 controls/13 pre-Huntington Disease); scanned 2 times about 2 years apart | Private | SPM5/GIFT | General linear model (GLM) analysis Data processing was performed. Contrasts were computed for the main effects of group and time as well as for group 9 time interactions, followed by post hoc t tests. Within-group changes over time were assessed using t tests for controls and preHD individuals. A spatial ICA was computed on the entire data set. To increase the stability of the components, we used the ICASSO algorithm. spatial sorting was performed twice: first, using MarsBar [38], a binarized mask was computed by combining within-group deactivation maps derived from the GLM analysis. Second, to validate the first spatial sorting procedure, sorting was additionally performedusing a DMN mask, as provided by the GIFT toolbox. To fully include those brain regions that were recruited by both groups over time, we masked between-group comparisons with a combined mask. This mask was computed using t test-derived composite TNN maps (thresholded at p\0.05) across both groups and time points. Longitudinal within-group changes in functional connectivity were assessed using t tests for controls and preHD. | 24071913 | |||||||||||||||||||||
26 | PMID: 23001254 | 2013 | 10.1007/s00213-012-2873-z | Memory-related hippocampal functioning in ecstasy and amphetamine users | 40 participants with a high probability of future amphetamine/ecstasy use, scanned two times 1 year apart; 12 controls, 17 users, 11 sporadic users | Private | SPM5 | The preprocessed data were analyzed using a two stage procedure for repeated measures ANOVA (Henson and Penny 2003). Separate analyses of the two identical encoding runs revealed similar activation patterns. Consequently, the encoding runs were summarized on the first level analysis. In an initial step, subject-specific changes in BOLD response were assessed using linear contrasts of the GLM parameters.To explore between-group differences at baseline, contrasts of task were entered into separate two-sample t-tests. Separate contrasts for the main effects of task and time (t1, t2) were computed for each subject. Main effects of the factors GROUP and TIME and interaction effects of the factors GROUP × TIME were computed entering the appropriate first level contrasts in two-sample t-tests. | 23001254 | |||||||||||||||||||||
27 | PMID: 20119495 PMCID: PMC2812907 | 2008 | 10.1007/s11682-008-9027-2. | Neural mechanisms underlying learning following semantic mediation treatment in a case of phonologic alexia | 1 male participant, scanned 3 times. T1: pre-treatment; T2: post-treatment (T1+4months); T3: post-overlearning(T2+5months) | Private | Matlab 7.0.4; SPM2 | In order to examine changes in lateralization index over time, we generated Lateralization Index (LI) curves by using the resulting t-maps from the contrast between (to be) over-learned and untrained words (ITP>IB) for each of the three time points in the LI-toolbox plug-in for spm2 (Wilke & Lidzba, 2007). Within the LI-toolbox we chose the bootstrapping algorithm (Wilke & Schmithorst, 2006) in order to obtain weighted mean values of lateralization. We then applied a global gray inclusive matter mask. The midline ± 5mm was excluded from the volumes to be investigated. Regional masks of the separate lobes were also calculated for analyses of their separate contributions to the LI. In order to correct for the presence of the lesion in one of the hemispheres we also used the clustering and variance weighting options within the toolbox. | 20119495 | |||||||||||||||||||||
28 | PMID: 28594850 PMCID: PMC5464555 | 2017 | 10.1371/journal.pone.0178017 | No changes in functional connectivity during motor recovery beyond 5 weeks after stroke; A longitudinal resting-state fMRI study | EXPLICIT-stroke Trial | Private | SPM 12/MATLAB | To evaluate time-dependent changes in rs-functional connectivity of motor networks, we calculated the mean connectivity between the ROIs comprising the motor system (average Z-score for all pairs of Caudal Middle Frontal, Paracentral, Postcentral, Precentral) within the ipsilesional and contralesional hemisphere, for the first and second session (week 5 and 26 post-stroke onset). This resulted in two connectivity values for each session, one for the ipsilesional, and one for the contralesional hemisphere. The same connectivity values were calculated for the single session of the healthy control subjects.To evaluate time-dependent changes in the motor network of patient, we performed a 2 x 2 repeated measures ANOVA with session (week 5 versus 26) and hemisphere (affected versus non-affected) as within subject factors. In addition, we investigated the relation between magnitude of time-dependent changes in rs-functional connectivity and time-dependent changes in motor impairment as measured with the FMA-UE. The change in FMA-UE between week 5 and week 26 was tested using a paired-samples t-test (two-sided). To investigate rs-functional connectivity differences per hemisphere between patients and control subjects, we compared rs-functional connectivity of the patients at week 5 post-stroke with the rs-functional connectivity of control subjects using a repeated measures ANOVA, with hemisphere (ipsilesional/contralesional) as within subjects-factor, and group (patient/control) as between subjects-factor. To further investigate potential changes in connectivity beyond the hypothesized areas, we performed an additional analysis. We tested differences in the connections between all ROI pairs between week 5 and week 26 after stroke using paired-samples t-tests, and between patients at week 5 and control subjects using paired samples t-tests (p <.05 with Bonferroni corrections for the number of tests, n = 29890: p<.00000167). Additionally, to measure overall effects instead of focussing on every corrected significant ROI, we also observed the actual proportion of significant tests while keeping the threshold at p<.05. | 28594850 | |||||||||||||||||||||
29 | PMID: 25771392 PMCID: PMC4419147 | 2015 | 10.1016/j.neurobiolaging.2015.02.001 | Normal-appearing cerebral white matter in healthy adults: Mean change over 2 years and individual differences in change | n=96 healthy adults, scanned at baseline and two-year follow-up | Private | Mplus 7 | structural equation modeling framework to assess 2-year change in DTI indices DTI indices while controlling for individual differences in age, vascular risk, and interscan interval length. We first fit a series of univariate, 2-occasion LCSMs | 25771392 | |||||||||||||||||||||
30 | PMID: 27001500 | 2016 | 10.1016/j.neuroimage.2016.03.029 | Reduced functional segregation between the default mode network and the executive control network in healthy older adults: A longitudinal study | baseline and 2-year follow-up | Private | Singapore-Longitudinal Ageing Brain Study (S-LABS) | Linear mixed models were performed in R using lme4, lmerTest, and effects to assess longitudinal analyses | We modeled the longitudinal changes in FC, GMV, and cognitive performance using linear mixed models that modeled fixed and random effects simultaneously, accounting for unequal sampling intervals, and missing data. For each participant j, the dependent variable Y (FC or cognitive score) was measured at each Time i, the longitudinal variable representing time interval since the first available session. The first available session of each participant was defined as the earliest session with quality task-free fMRI data or neuropsychological assessment.Time always started from zero. The longitudinal ageing effect was expressed as a simple regression between Time and Y, plus a residual r. Yij = β0j + β1j (Timei) +rij. Gender was a binary dummy variable, while Age and Education were the grand-mean-centered versions of the respective variables. Replacing the corresponding terms in Eq. (1) with Eqs. (2a) and (2b) resulted in Eq. (3). While equivalent to Eqs. (1) and (2a) and (2b), it highlights the cross-level interaction effects (Morrell et al., 2009): the intercepts (β0j) and the longitudinal changes (slopes β1j) were different for each participant (random effect μs) and this difference might be explained by individual differences (fixed effects γs). Specifically, ageing (Time) may proceed at different rates depending on cohort (Age), i.e., γ11(Agej*Timeij). Using the proposed linear mixed model, we first examined the longitudinal ageing effects on ICN (Fig. 1, step 2). Each of the intranetwork and inter-network FC was modeled separately. This was followed by modeling of each of the five cognitive domains (Fig. 1,step 3) as well as the GMV of each of the three ICN in the same fashion.To focus on ageing effects, we primarily reported longitudinal effects related to Time (β1j), i.e., γ10 and γ11 (Eqs. (2b) and (3)), followed by statistically significant cross-sectional age effects related to Age, i.e., γ03 (Eqs. (2a) and (3)), if any, in the same models (see also Supplementary Materials S5). | 27001500 | ||||||||||||||||||||
31 | PMID: 24589297 | 2013 | 10.1111/adb.12111 | Relationship between working-memory network function and substance use: A 3-year longitudinal fMRI study in heavy cannabis users and controls | 3-year longitudinal study investigating the effect of neurocognitive processes on the course of cannabis use in heavy cannabis users | Private | Custom code? | Tensor-ICA (Beckmann & Smith 2005) was used to investigate working-memory network functionality within and between groups over time. RTs and accuracy were analyzed using rANOVAs with group as between-subject factor and memory-load (0-back, 1-back and 2-back) and time (baseline, follow-up) as within-subject factors. Also, ROI GLM analyses indicated that activity amplitude of areas within the network did not significantly change over time. | 24589297 | |||||||||||||||||||||
32 | PMID: 26517540 PMCID: PMC4627782 | 2015 | 10.1371/journal.pone.0140134 | Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years | 1 healthy adult male | MyConnectome | http://myconnectome.org/wp/data-sharing | Custom code (python) | The spatial similarity of each week’s RSN spatial maps to the group mean map, as calculated using η^2, was obtained as an outcome measure. for each type of RSN outcome measure (i.e., spatial similarity of RSN maps, temporal fluctuation magnitude, and BNC), intrasubject inter-session reproducibility was characterized using coefficient of variation (CV),defined as the ratio of standard deviation (SD) to mean, expressed in percentage. CV enables the comparison of data sets with different means, by providing a standardized measure of dispersion. However, the calculated CV can appear artificially inflated if a mean value of a data set is close to zero. Therefore, in order to help keep things in perspective, we also report corresponding SD values. | 26517540 | ||||||||||||||||||||
33 | PMID: 24931140 | 2014 | 10.1111/ejn.12640 | Significance of longitudinal changes in the default-mode network for cognitive recovery after stroke | The patients had to (i) have experienced their first-ever stroke, (ii) have suffered a right-hemispheric lesion and (iii) be younger than 70 years old. Of the 24 stroke patients initially enrolled, 11 (eight males, 55.73 � 8.69 years of age) completed three fMRI sessions, and seven of these patients completely finished the comprehensive neuropsychological test battery over a 6-month period (Table 1). Eleven age- and sex-matched healthy subjects (nine males, 56.23 � 2.89 years of age) were recruited. | Private | Custom code/doesn't say | One-sample t-tests were calculated on the best-matched DMN independent component of each month for 11 stroke patients and 11 healthy volunteers. A height threshold of P < 0.05 corrected for multiple comparisons across the whole brain was applied, with k > 10 voxels. ANOVA was conducted to compare the differences between the stroke patients and healthy volunteers for each respective month using the P < 0.05 threshold corrected for multiple comparisons across the whole brain. Changes in DMN connectivity were observed over 6 months in seven stroke patients who completed three rounds of SCNT, and an ROI approach was taken when investigating the relationship between the alterations of connectivity and cognitive improvement. Repeated-measures ANOVA was performed to determine the ROIs, which showed linearly increased and decreased connectivity over time (Table 3). The average z-scores of the defined ROIs (Fig. 3A, Table 3) were extracted using the MarsBaR toolbox (http://mars-bar.sourceforge.net/) in the normalized images. Correlation analysis was performed between the z-score difference of each ROI (z-score at 3 months z-score at 1 month; z-score at 6 months z-score at 3 months) and the delta score of the cognitive function tests (score at 3 months score at 1 month; score at 6 months score at 3 months), respectively, to determine the relationship between the changes in cognitive function and changes in connectivity. | 24931140 | |||||||||||||||||||||
34 | PMID: 22629335 PMCID: PMC3356348 | 2012 | 10.1371/journal.pone.0036838 | The baseline and longitudinal changes of PCC Connectivity in mild cognitive impairment: A combined structure and resting-state fMRI study | Thirty-two right-handed subjects (16 MCI patients and 16 healthy elders) participated in this study. The MCI subjects were recruited from patients who had consulted a memory clinic for memory problems at Xuanwu Hospital, Beijing, China. The healthy elderly controls were recruited from the local community through advertisements. | Private | AFNI (AlphaSim) | The z values were also entered into a random effect two-sample t-test to identify the regions showing significant differences in connectivity to PCC between 14 MCI patients and 14 healthy controls, and between MCI1 and MCI2. In order to explore whether PCC connectivity varies with the disease progression and memory performances in MCI patients, a further correlation analysis between the PCC connectivity and neuropsychological performances was done. First, averaged z-values of each cluster with significant group differences were extracted. Then, Pearson’s correlative analysis were performed to examine the possible relationships between the z-values and neuropsychological performances [California verbal learning test (CVLT): Immediate Recall, Short Delayed Recall, Long Delayed Recall, and MMSE] in MCI patients. | 22629335 | |||||||||||||||||||||
35 | PMID: 21584904 | 2011 | 10.1002/hipo.20944 | The hippocampus is involved in mental navigation for a recently learned, but not a highly familiar environment: A longitudinal fMRI study | 8 of 13 participants scanned twice a year apart. Remaining 5 only scanned once. | Private | AFNI | Accuracy (percentage correct) was analyzed with a 2x3x4 repeated measures analysis of variance (ANOVA) with session (session 1, session 2) and task as factors. The mean percentage signal change was extracted for each task and for each session. A paired-sample t-test revealed a significant decrease in the mean percentage signal change from session 1 to session 2. Subsequent group analysis, consisting of a voxel-wise, mixed model, two-factor ANOVA with participants as a random factor and task as a fixed factor. A conjunction analysis of the distance judgment, proximity judgment, and blocked-route problem-solving tasks in session 1 and session 2 was also done. | 21584904 | |||||||||||||||||||||
36 | PMID: 10860798 | 2000 | 10.1006/nimg.2000.0562 | Variability in fMRI: An examination of intersession differences | A single young adult, scanned 99 times | Private | SPM99 | GLM: Multiple-Session Analyses - Session-by-Condition Interactions; Multiple-Session Analyses - Fixed-Effects Model; Multiple-Session Analyses - Random-Effects Model | 10860798 | |||||||||||||||||||||
37 | PMID: 25418860 | 2015 | 10.1002/hbm.22700 | Virtual Water Maze Learning in Human Increases Functional Connectivity Between Posterior Hippocampus and Dorsal Caudate | n=16 female participants were scanned before and after one week of task training | Private | Statistica 9 | Learning-related changes in functional connectivity between each hippocampus-striatum pair was assessed by entering Fisher’s r-to-z scores from across the four resting-state runs into a repeated measures ANOVA (2 sessions 3 2 phases). | 25418860 | |||||||||||||||||||||
38 | PMID: 26545457 PMCID: PMC4848116 | 2015 | 10.1016/j.neuroimage.2015.10.085 | White matter and memory in healthy adults: Coupled changes over two years | n=96 healthy adults, scanned at baseline and two-year follow-up | Private | Mplus 7 | To test the mutual relations between changes in WM and cognition, we employed latent change score models, a structural equation modeling | 26545457 | |||||||||||||||||||||
39 | ||||||||||||||||||||||||||||||
40 | ||||||||||||||||||||||||||||||
41 | ||||||||||||||||||||||||||||||
42 | ||||||||||||||||||||||||||||||
43 | ||||||||||||||||||||||||||||||
44 | ||||||||||||||||||||||||||||||
45 | ||||||||||||||||||||||||||||||
46 | ||||||||||||||||||||||||||||||
47 | ||||||||||||||||||||||||||||||
48 | ||||||||||||||||||||||||||||||
49 | ||||||||||||||||||||||||||||||
50 | ||||||||||||||||||||||||||||||
51 | ||||||||||||||||||||||||||||||
52 | ||||||||||||||||||||||||||||||
53 | ||||||||||||||||||||||||||||||
54 | ||||||||||||||||||||||||||||||
55 | ||||||||||||||||||||||||||||||
56 | ||||||||||||||||||||||||||||||
57 | ||||||||||||||||||||||||||||||
58 | ||||||||||||||||||||||||||||||
59 | ||||||||||||||||||||||||||||||
60 | ||||||||||||||||||||||||||||||
61 | ||||||||||||||||||||||||||||||
62 | ||||||||||||||||||||||||||||||
63 | ||||||||||||||||||||||||||||||
64 | ||||||||||||||||||||||||||||||
65 | ||||||||||||||||||||||||||||||
66 | ||||||||||||||||||||||||||||||
67 | ||||||||||||||||||||||||||||||
68 | ||||||||||||||||||||||||||||||
69 | ||||||||||||||||||||||||||||||
70 | ||||||||||||||||||||||||||||||
71 | ||||||||||||||||||||||||||||||
72 | ||||||||||||||||||||||||||||||
73 | ||||||||||||||||||||||||||||||
74 | ||||||||||||||||||||||||||||||
75 | ||||||||||||||||||||||||||||||
76 | ||||||||||||||||||||||||||||||
77 | ||||||||||||||||||||||||||||||
78 | ||||||||||||||||||||||||||||||
79 | ||||||||||||||||||||||||||||||
80 | ||||||||||||||||||||||||||||||
81 | ||||||||||||||||||||||||||||||
82 | ||||||||||||||||||||||||||||||
83 | ||||||||||||||||||||||||||||||
84 | ||||||||||||||||||||||||||||||
85 | ||||||||||||||||||||||||||||||
86 | ||||||||||||||||||||||||||||||
87 | ||||||||||||||||||||||||||||||
88 | ||||||||||||||||||||||||||||||
89 | ||||||||||||||||||||||||||||||
90 | ||||||||||||||||||||||||||||||
91 | ||||||||||||||||||||||||||||||
92 | ||||||||||||||||||||||||||||||
93 | ||||||||||||||||||||||||||||||
94 | ||||||||||||||||||||||||||||||
95 | ||||||||||||||||||||||||||||||
96 | ||||||||||||||||||||||||||||||
97 | ||||||||||||||||||||||||||||||
98 | ||||||||||||||||||||||||||||||
99 | ||||||||||||||||||||||||||||||
100 |