Cortical gyrification morphology in individuals with ASD and ADHD across the lifespan: a systematic review and meta-analysis

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are common neurodevelopmental disorders (NDDs) that may impact brain maturation. A number of studies have examined cortical gyrification morphology in both NDDs. Here we review and when possible pool their results to better understand the shared and potentially disorder-specific gyrification features. We searched MEDLINE, PsycINFO, and EMBASE databases, and 24 and 10 studies met the criteria to be included in the systematic review and meta-analysis portions, respectively. Meta-analysis of local Gyrification Index (lGI) findings across ASD studies was conducted with SDM software adapted for surface-based morphometry studies. Meta-regressions were used to explore effects of age, sex, and sample size on gyrification differences. There were no significant differences in gyrification across groups. Qualitative synthesis of remaining ASD studies highlighted heterogeneity in findings. Large-scale ADHD studies reported no differences in gyrification between cases and controls suggesting that, similar to ASD, there is currently no evidence of differences in gyrification morphology compared with controls. Larger, longitudinal studies are needed to further clarify the effects of age, sex, and IQ on cortical gyrification in these NDDs.info:eu-repo/semantics/publishedVersio

Feasibility study of TSPO quantification with [18F]FEPPA using population-based input function.

The input function (IF) is a core element in the quantification of Translocator protein 18 kDa with positron emission tomography (PET), as no suitable reference region with negligible binding has been identified. Arterial blood sampling is indeed needed to create the IF (ASIF). In the present manuscript we study individualization of a population based input function (PBIF) with a single arterial manual sample to estimate total distribution volume (VT) for [18F]FEPPA and to replicate previously published clinical studies in which the ASIF was used.The data of 3 previous [18F]FEPPA studies (39 of healthy controls (HC), 16 patients with Parkinson's disease (PD) and 18 with Alzheimer's disease (AD)) was reanalyzed with the new approach. PBIF was used with the Logan graphical analysis (GA) neglecting the vascular contribution to estimate VT. Time of linearization of the GA was determined with the maximum error criteria. The optimal calibration of the PBIF was determined based on the area under the curve (AUC) of the IF and the agreement range of VT between methods. The shape of the IF between groups was studied while taking into account genotyping of the polymorphism (rs6971).PBIF scaled with a single value of activity due to unmetabolized radioligand in arterial plasma, calculated as the average of a sample taken at 60 min and a sample taken at 90 min post-injection, yielded a good interval of agreement between methods and optimized the area under the curve of IF. In HC, gray matter VTs estimated by PBIF highly correlated with those using the standard method (r2 = 0.82, p = 0.0001). Bland-Altman plots revealed PBIF slightly underestimates (~1 mL/cm3) VT calculated by ASIF (including a vascular contribution). It was verified that the AUC of the ASIF were independent of genotype and disease (HC, PD, and AD). Previous clinical results were replicated using PBIF but with lower statistical power.A single arterial blood sample taken 75 minute post-injection contains enough information to individualize the IF in the groups of subjects studied; however, the higher variability produced requires an increase in sample size to reach the same effect size

Total distribution volume (<i>V</i>_<i>T</i>) calculated using ASIF (2-TCM) and PBIF75 (Logan plot).

<p>In Temporal, Prefrontal, Hippocampus, Parietal and Occipital regions stratified as HAB and MAB groups. ASIF showed an average reduction of 29% in <i>V</i>_<i>T</i> across ROIs while PBIF showed a reduction of 26%. 5/7 ROIs survived the multiple region Bonferroni adjustment using ASIF and only 2/7 passed the Bonferroni adjustment using PBIF75. Images were partial volume effect corrected prior to TAC extraction. * p< 0.05, ** p<0.01 *** p< 0.001.</p

Comparison of [¹⁸F]FEPPA regional total distribution volume (<i>V</i>_<i>T</i>) between healthy control subjects and AD patients calculated with PBIF75 for HABs (left) and MABs (right).

<p>(<i>cf</i>. figure 1 in ref [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177785#pone.0177785.ref007" target="_blank">7</a>]). PBIF75 estimation led to the same conclusion as the previously published with ASIF: [¹⁸F]FEPPA <i>V</i>_<i>T</i> in AD patients is on average 13–48% higher than HC (A Factorial ANOVA with genotype and age as covariate showed: p<0.05 in the Temporal, Prefrontal, Parietal and Occipital and p = 0.4 in the hippocampus). Images were partial volume effect corrected prior to TAC extraction. * p< 0.05 in ANOVA within group.</p

Population based input function.

<p>A) Average (n = 24) time evolution of unmetabolized [¹⁸F]FEPPA in plasma from HC (n = 8), PD (n = 8) and AD (n = 8) subjects. The inner plot shows details of the first 100 seconds post injection. B) The area under the curve of the input functions created from arterial blood sampling did not differ between 21 HC, 18 AD and 16 PD.</p

Comparison of total distribution volume (<i>V</i>_<i>T</i>) between healthy controls and PD patients calculated with ASIF and PBIF75 for putamen (left) and caudate (right).

<p>Estimation with PBIF75 led to same conclusion as with the ASIF; that there is no difference from disease (p = 0.2 in Caudate and p = 0.8 in the Putamen using PBIF vs p = 0.4 in Caudate and p = 0.7 in Putamen using ISIF) and that there is a difference due to genotype (p = 0.001 in Caudate and p = 0.002 in Putamen using PBIF vs p = 0.001 in the Caudate and p = 0.0008 in Putamen using ASIF). Images were partial volume effect corrected prior to TAC extraction.</p

Bland-altman plot of <i>V</i>_<i>T</i> assessed by ASIF (2-TCM) and PBIF75 (Logan plot) for 7 regions of interest in healthy controls.

<p>FEPPA <i>V</i>_<i>T</i><i>s</i> derived by Logan PBIF75 plot slightly underestimated (Bias < 1 mL/cm³) those derived with 2-TCM. (A) In MABs (n = 7), the 95% limits of agreement (dashed <i>lines</i>) was between -0.7 to 1.3 mL/cm³ and no systematic bias was observed (r = 0.12, p = 0.6). (B) In HABs (n = 14), the 95% limits of agreement (dashed lines) was between -2.2 to 4.2 mL/cm³ and no systematic bias was observed (r = 0.08, p = 0.7).</p

Comparison of the AUC of the PBIF and ASIF at 60 and 120 min as a function of the time-sample used to rescale.

<p>Samples at 75 min minimize the difference and SD. It should be noted that sample at 52 min is the average between 45and 60 min and 75 is the average between 60 and 90 min. the result is the average between 21 HC, 18 AD and 16 PD.</p

Cortical Gyrification Morphology in ASD and ADHD: Implication for Further Similarities or Disorder-Specific Features?

Shared etiological pathways are suggested in ASD and ADHD given high rates of comorbidity, phenotypic overlap and shared genetic susceptibility. Given the peak of cortical gyrification expansion and emergence of ASD and ADHD symptomology in early development, we investigated gyrification morphology in 539 children and adolescents (6-17 years of age) with ASD (n=197) and ADHD (n=96) compared to typically developing controls (n=246) using the local Gyrification Index (lGI) to provide insight into contributing etiopathological factors in these two disorders. We also examined IQ effects and functional implications of gyrification by exploring the relation between lGI and ASD and ADHD symptomatology beyond diagnosis. General Linear Models yielded no group differences in lGI, and across groups, we identified an age-related decrease of lGI and greater lGI in females compared to males. No diagnosis-by-age interactions were found. Accounting for IQ variability in the model (n=484) yielded similar results. No significant associations were found between lGI and social communication deficits, repetitive and restricted behaviours, inattention or adaptive functioning. By examining both disorders and controls using shared methodology, we found no evidence of atypicality in gyrification as measured by the lGI in these conditions