The prevalence of autism spectrum disorder (ASD) among children in the United States has risen dramatically over the past two decades. In 2022, an estimated 32.2 out of every 1,000 8-year-old children were identified with ASD, marking a nearly fivefold increase from the rate of 6.7 per 1,000 children in 2000. This significant upward trend underscores the growing importance of understanding and addressing ASD in American society. Gender disparities in autism diagnosis The increase in ASD prevalence is not uniform across genders. From 2016 to 2019, male children were nearly four times more likely to be diagnosed with ASD than their female counterparts. Approximately 4.8 percent of boys aged 3 to 17 years had received an ASD diagnosis at some point in their lives, compared to only 1.3 percent of girls in the same age group. This substantial gender gap highlights the need for further research into potential biological and social factors influencing ASD diagnosis rates. Racial and ethnic variations in autism prevalence Autism prevalence also varies across racial and ethnic groups. Data from 2016 to 2019 show that non-Hispanic white children aged 3 to 17 years had an ASD prevalence of 2.9 percent, while around 3.5 percent of Hispanic children had ASD. While this statistic provides insight, it is essential to consider potential disparities in diagnosis and access to services among different racial and ethnic communities. Further research and targeted interventions may be necessary to ensure equitable identification and support for children with ASD across all populations.

The prevalence rate of autism spectrum disorder among white, non-Hispanic eight-year-olds in Georgia was estimated to be ** per 1,000 children as of 2022. Autism spectrum disorder is a developmental disability characterized by deficits in social communication and interaction as well as repetitive behavior, interest, or activity patterns. This statistic displays the estimated prevalence of autism spectrum disorder among children aged eight years in selected U.S. states in 2022, by race/ethnicity.

The prevalence rate of autism spectrum disorder among four-year-old children in Missouri was around 24.8 per 1,000 children in 2022. Autism spectrum disorder is a developmental disability characterized by deficits in social communication and interaction as well as repetitive behavior, interest, or activity patterns. This statistic displays the estimated prevalence of autism spectrum disorder among children aged four years in select U.S. states in 2022.

The prevalence rate of autism spectrum disorder among male children aged eight years in Georgia was estimated to be around ** per 1,000 children as of 2022. Autism spectrum disorder is a developmental disability characterized by deficits in social communication and interaction as well as repetitive behavior, interest, or activity patterns. This statistic displays the estimated prevalence of autism spectrum disorder among children aged 8 years in select U.S. states in 2022, by gender.

The prevalence rate of autism spectrum disorder among children aged eight years in Missouri was **** per 1,000 children in 2010. In 2022, this rate was estimated to be **** per 1,000 eight-year-olds. Autism spectrum disorder is a developmental disability characterized by deficits in social communication and interaction as well as repetitive behavior, interest, or activity patterns. This statistic displays the estimated prevalence of autism spectrum disorder among children aged 8 years in select U.S. states from 2010 to 2022.

The prevalence rate of autism spectrum disorder among white, non-Hispanic four-year-old children in California was 31 per 1,000 in 2020. Autism spectrum disorder is a developmental disability characterized by deficits in social communication and interaction as well as repetitive behavior, interest, or activity patterns.This statistic displays the estimated prevalence of autism spectrum disorder among children aged four years in selected U.S. states in 2020, by race/ethnicity.

Background: Increased incidence and prevalence of autism spectrum disorder (ASD) over the last two decades have prompted considerable efforts to investigate its etiological factors. We examined an association between month of birth and ASD incidence. Methods: In a retrospective cohort of male children born from January 1999 to December 2008 in a large health organization in Israel (Maccabi Healthcare Services), ASD was followed from birth through December 2015. Results: Of 108,548 boys, 975 cases of ASD were identified. The highest rates (10.3 and 10.2 per 1000 male live births) were recorded for children born in May and August, and the lowest rates for February (7.6 per 1000 male live births). Among lower socioeconomic status households, boys born in August were more likely (OR=1.71; 95% confidence interval: 1.06 – 2.74) of being diagnosed with ASD than children born in January. Significantly higher rates were not observed for other months. Conclusions: In line with several previous studies, we found modestly higher likelihood of autism occurrence among male children of lower socioeconomic levels born in August.

Experimental designWe back-translated a task used in human studies??4? and developed a novel Go/No-go detection task for vibrotactile stimuli which we combined with two-photon calcium imaging of excitatory and inhibitory neurons in the forepaw-related primary somatosensory cortex (FP-S1) to study altered tactile perception in autism and define its neural correlates. MiceSecond-generation Fmr1 knockout (Fmr1−/y) and wild-type littermate mice 5-16 weeks old were used in our study. Mice were maintained in a mixed 129/Sv/C57Bl/6 J/FVB background (backcrossed 6 generations into C57Bl/6J) as described in ref.??28,34?. Male wild-type and Fmr1–/y littermates were generated by crossing Fmr1+/− females with Fmr1+/y male mice from the same production, and the resulting progeny used for our experiments was either Fmr1+/y (wild type) or Fmr1–/y (KO). Mice were maintained in collective cages following weaning (2-4 litter males per cage). Cages were balanced for genotype and supplemented with cotton nestlets and carton tubes. Number of mice are given in the figure captions. The genotype of experimental animals was re-confirmed post hoc by tail-PCR.SurgeryMice (P33-42) were anaesthetized with isoflurane (4.5% induction, 1.5-2% maintenance). Proper depth of anesthesia was monitored by testing the absence of a foot-pinch reflex and whisker movement. Mice were head-fixed using non-puncture ear-bars and a nose-clamp, and optic gel (Ocry-gel) was applied to both eyes. Body temperature was maintained at 37 °C with a heating pad and rectal probe. Prior to the beginning of the surgery, saline (0.1 ml/ 10 g) and 0.1 ml of a 1:10 Buprenorphine to saline solution were administered subcutaneously. Following hair trimming and application of Betadine scrub and Betadine dermal solution at the scalp, 0.1 ml of a 1:4 Lidocaine to saline solution was administered subcutaneously and waited for 2–5 min to induce local analgesia. The scalp was carefully removed and a 3 mm diameter craniotomy was made above the S1 forepaw region (0.75 mm anterior and -2.5 mm lateral from Bregma, confirmed with intrinsic imaging coupled with forepaw stimulation) using a dental drill (World Precision Instruments). The cortical surface was rinsed with saline throughout the surgery. Stereotactic injections of 100 nl of AAV1/2-syn-jGCaMP8m (titre: 2.6E+12 gcp/ml, diluted 1:4 in Hank’s Balanced Salt Solution) and AAV1/2-mDlx-HBB-chI-mRuby3-SV40p(A) (titre: 8.6E+13 gcp/ml, diluted 1:2 in Hank’s Balanced Salt Solution) were delivered (50 nl/ min) in layers 2/3 (z depth 0.3mm). A double coverslip, composed of a 3 mm diameter lower glass and a 4 mm diameter upper glass glued together with optical adhesive (NOA 61, Norland Products), was implanted on top of the craniotomy and secured in place with cyanoacrylate glue. A head-post was attached to the skull with cyanoacrylate glue and secured with dental cement. An optical glue (OptiBond Universal, Kerr) and dental filling material (Charisma, Kulzer) were applied on the skull and curated with LED blue light. Dental cement was applied on the top. After the surgery the mice were placed on a warm blanket for 1 h after they awoke from anesthesia.Go/No-Go vibrotactile decision-making taskSetupThe vibrotactile decision-making setup was positioned in an isolation cubicle to minimize interference during the experiment. Mice were placed in a body tube and were head-fixed with their forepaws resting on two steel bars (6 mm diameter, Thorlabs). The right bar was mounted to a Preloaded Piezo Actuator (P-841.6, Physik Instrumente) equipped with a strain gauge feedback sensor and controlled(E-501, Physik Instrumente) in a closed loop, as described before??50?. Water reward was delivered through a metal feeding needle (20G, 1,9mm tip, Agntho's AB) connected to a lickport interface with a solenoid valve (Sanworks) equipped with a capacitive sensor . The behavioral setup was controlled by Bpod (Sanworks) through scripts in Python .Habituation to head-fixation and water restrictionMice (P40-P50) were handled using carton tubes and the cupping technique until they were comfortable in the experimenter’s hands, attested by eating while handled. Mice were gradually habituated to the experimental setup and head fixation for 5 days. The third day of habituation, a water-restriction protocol was implemented, where mice had access to liquid water in the setup and to a solid water supplement (Hydrogel, BioServices) in their home cage. In total, the animals received 1.5-2 ml of water per day, which corresponds to 50-65% of their ad libitum consumption, while ensuring that they did not lose more than 10% of their weight. Each mouse received 6-8 g of Hydrogel (ad libitum) during the weekend. This water restriction protocol was maintained throughout behavioral training and until the end of behavioral testing.Go/No-Go vibrotactile task training and testingHabituated mice (8 weeks old) were trained to associate vibrotactile stimulus delivery (pure sinusoid, 500 ms duration, 15µm amplitude, 40 Hz frequency) with a water reward (8 µl). Go trials consisted of stimulus delivery followed by a 2 s response window during which the mice could lick to receive the reward. No-Go (catch) trials included no stimulation and licking during these trials resulted in a 5 s timeout before the beginning of the following trial. Inter-trial intervals were variable (5-10 s). Training was subdivided in 3 phases: (a) automatic water delivery at the beginning of the response window (b) Pre-training: lick-triggered water delivery during Go trials and timeout delivery during No-Go trials (c) Training: lick-triggered water delivery during Go trials when the mouse did not lick during the inter-trial interval (3-8 s) before the stimulation. No stimulus delivery and timeout (5 s) if the animal licked during the inter-trial interval (3-8 s) before the stimulus and timeout delivery (5 s) during No-Go trials. All sessions consisted of 300 trials with 70-80% Go and 20-30% No-Go proportion. All trials were presented in a pseudorandom sequence. Pilot experiments with an extra sensor to monitor forepaw placement confirmed that the mice did not remove their forepaws from the bar before stimulus delivery. To complete Pre-training, mice needed to complete 80% of successful Go trials and reduce their spontaneous licking during No-Go trials below 40%. To complete Training, mice needed to reach the criterion of more than 80% successful Go-trials and less than 30% unsuccessful No-Go trials as an average for 3 consecutive days. All mice that fulfilled this criterion were tested for the detection of vibrotactile stimuli of differing amplitudes that they haven’t experienced before. During testing a 90% Go:10% No-Go ratio was delivered. Go trials consisted of 6 vibrotactile stimuli (pure sinusoid, 500 ms duration, 10 Hz frequency, 2-4-6-8-10-12 µm amplitude), delivered in a pseudorandom manner.Go/No-Go vibrotactile task analysisAll analysis was performed with custom-made Python scripts. Behavior was quantified based on the lick events and four main outcomes were measured: Hit and Miss rate for successful and unsuccessful Go trials respectively (number of Hit or Miss divided by the total number of Go trials where a stimulus was delivered), Correct Rejection and False Alarm rate for successful and unsuccessful No-Go trials respectively (number of Correct Rejections or False alarms divided by the total number of No-Go trials). Prestimulus spontaneous licking was calculated as Timeout events during Go trials divided by the total number of Go trials. Training duration was calculated based on the total number of days each animal passed in pre-training and training. For testing, only sessions with less than 40% unsuccessful No-Go trials were analyzed. Psychometric curves were fitted on the Hit rate for each stimulus amplitude using a general linear model. An average of 140 repetitions for each amplitude was used to calculate Hit rates. Detection thresholds were calculated based on the psychometric curves, as the stimulus that the mice detect 50% of the time.Adhesive tape removal testMice (11-13 weeks old) were handled for one day and underwent three days of gradual habituation to restriction and to a round Plexiglas arena (14 cm diameter) Two patches of adhesive tape were attached to their forepaws as described before (Bouet et al., 2009). The time before the first contact of each tape and the total time necessary for their removal was calculated manually using a Python script.Two-photon calcium imagingSetupMeasurements were performed with a Femtonics FEMTOSmart microscope coupled with a widely tunable, femtosecond Ti:Sapphire Laser (Chameleon Ultra, Coherent) pumped by a solid-state laser (Verdi 18W, Coherent). Excitation at 920 nm was used for GCaMP8m and at 1000 nm for mRuby3and two photomultiplier tubes were used for the collection of green and red fluorescence. Laser power was modulated with pockels cells. A 20x/1 NA objective (Zeiss) was used to obtain512 × 512 pixel images covering 480 × 480 μm of cortex were acquired at 30.96 Hz using the resonant scanner system (Femtonics). Measurements were written as series of 32 bit images and saved as ome files. Analog and digital inputs sent by Bpod and the piezoelectric sensor controller were collected and saved as timestamps by the microscope and were used to synchronize the behavioral events with the calcium traces from imaging.

The prevalence rate of autism spectrum disorder among male children aged 4 years old in Georgia was **** per 1,000 children in 2020. Autism spectrum disorder is a developmental disability characterized by deficits in social communication and interaction as well as repetitive behavior, interest, or activity patterns. This statistic displays the estimated prevalence of autism spectrum disorder among children aged 4 years in select U.S. states in 2020, by gender.

ABIDE I FreeSurfer 6 'local gyrification index' (lGI) data

DOI of this dataset: 10.5281/zenodo.7132610

This directory 'abide_freesurfer6_lgi' contains the ABIDE I FreeSurfer 6 'local gyrification index' (lGI) data and meshes.

Credits

This data is derived from the MRI scans of the ABIDE I dataset:

• ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/

Quoting from that website:

"The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first
 ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17
 international sites, sharing previously collected resting state functional
 magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets
 made available for data sharing with the broader scientific community.
 This effort yielded 1112 dataset, including 539 from individuals with
 ASD and 573 from typical controls (ages 7-64 years, median 14.7 years
 across groups). This aggregate was released in August 2012. Its
 establishment demonstrated the feasibility of aggregating resting
 state fMRI and structural MRI data across sites; the rate of these
 data use and resulting publications (see Manuscripts) have shown its
 utility for capturing whole brain and regional properties of the brain
 connectome in Autism Spectrum Disorder (ASD). In accordance with
 HIPAA guidelines and 1000 Functional Connectomes Project / INDI
 protocols, all datasets have been anonymized, with no protected
 health information included."

Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

How this data was produced

The following steps were used to create the data:

• We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).
• We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.
  • We did not run any quality metrics on the scans or exclude any subjects.
• We computed pial-lgi (Schaer et al. 2008, https://doi.org/10.1109/TMI.2007.903576) as implemented in FreeSurfer 6 for all subjects.
  • For some of the subjects, MRI data was not available or lgi could not be computed due to very bad quality of the (or a completely failed) surface reconstruction. These subjects are listed in the file 'subjects_lgi_computation_failed.txt' (14 of 1035 subjects).
  • All subjects for which lgi computation succeeded for both hemispheres are listed in the file 'subjects.txt' (1021 of 1035 subjects).

Contained files

• In order to reduce the size of this dataset, for each subject, we only included the following files:
  • /surf/lh.pial : the pial surface mesh for the left hemisphere, in FreeSurfer surf format.
  • /surf/rh.pial : the pial surface mesh for the right hemisphere, in FreeSurfer surf format.
  • /surf/lh.pial_lgi : the per-vertex lgi values for the left hemisphere, in FreeSurfer curv format.
  • /surf/rh.pial_lgi : the per-vertex lgi values for the right hemisphere, in FreeSurfer curv format.

See the section 'How this data was produced' for information on the files 'subjects.txt' and 'subjects_lgi_computation_failed.txt'.

What is NOT contained

• The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.
• The lgi values are only contained in native space. Standard space data is available as a separate download on Zenodo.

Author and License

Note: For the authors of the original ABIDE I dataset, see the Credits section above.

This lgi data was created by:

Dr. Tim Schäfer
Postdoc Computational Neuroimaging
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy
University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
http://rcmd.org/ts

The data is published under the following license:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.

See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

ABIDE I FreeSurfer 6 'native space descriptors' data

This archive contains the following ABIDE I FreeSurfer 6 native space mesh descriptors: thickness, area, volume, sulc, curv, jacobian_white.

Credits

This data is derived from the MRI scans of the ABIDE I dataset:

• ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/

Quoting from that website:

"The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first
 ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17
 international sites, sharing previously collected resting state functional
 magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets
 made available for data sharing with the broader scientific community.
 This effort yielded 1112 dataset, including 539 from individuals with
 ASD and 573 from typical controls (ages 7-64 years, median 14.7 years
 across groups). This aggregate was released in August 2012. Its
 establishment demonstrated the feasibility of aggregating resting
 state fMRI and structural MRI data across sites; the rate of these
 data use and resulting publications (see Manuscripts) have shown its
 utility for capturing whole brain and regional properties of the brain
 connectome in Autism Spectrum Disorder (ASD). In accordance with
 HIPAA guidelines and 1000 Functional Connectomes Project / INDI
 protocols, all datasets have been anonymized, with no protected
 health information included."

Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

How this data was produced

The following steps were used to create the data:

• We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).
• We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.
  • We did not run any quality metrics on the scans or exclude any subjects.

Contained files

• In order to reduce the size of this dataset, for each subject, we only included the following files:
  • /surf/lh.thickness : the per-vertex cortical thickness for the left hemisphere
  • /surf/rh.thickness : the per-vertex cortical thickness for the right hemisphere
  • /surf/lh.area : the per-vertex area of the white surface for the left hemisphere
  • /surf/rh.area : the per-vertex area of the white surface for the right hemisphere
  • /surf/lh.volume : the per-vertex cortical volume for the left hemisphere
  • /surf/rh.volume : the per-vertex cortical volume for the right hemisphere
  • /surf/lh.sulc : the per-vertex sulcal depth for the left hemisphere
  • /surf/rh.sulc : the per-vertex sulcal depth for the right hemisphere
  • /surf/lh.curv : the per-vertex mean curvature of the white surface for the left hemisphere
  • /surf/rh.curv : the per-vertex mean curvature of the white surface for the right hemisphere
  • /surf/lh.jacobian_white : the per-vertex jacobian for the left hemisphere
  • /surf/rh.jacobian_white : the per-vertex jacobian for the right hemisphere

All files are in binary FreeSurfer curv format.

What is NOT contained

• The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.

Author and License

Note: For the authors of the original ABIDE I dataset, see the Credits section above.

This lgi data was created by:

Dr. Tim Schäfer
Postdoc Computational Neuroimaging
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy
University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
http://rcmd.org/ts

The data is published under the following license:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.

See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

ABIDE I FreeSurfer 6 'brain volume' data

This archive contains the following ABIDE I FreeSurfer 6 volumes: mri/brain.mgz, mri/brain_mask.mgz, mri/aseg.mgz, mri/wm.mgz.

Credits

This data is derived from the MRI scans of the ABIDE I dataset:

• ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/

Quoting from that website:

"The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first
 ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17
 international sites, sharing previously collected resting state functional
 magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets
 made available for data sharing with the broader scientific community.
 This effort yielded 1112 dataset, including 539 from individuals with
 ASD and 573 from typical controls (ages 7-64 years, median 14.7 years
 across groups). This aggregate was released in August 2012. Its
 establishment demonstrated the feasibility of aggregating resting
 state fMRI and structural MRI data across sites; the rate of these
 data use and resulting publications (see Manuscripts) have shown its
 utility for capturing whole brain and regional properties of the brain
 connectome in Autism Spectrum Disorder (ASD). In accordance with
 HIPAA guidelines and 1000 Functional Connectomes Project / INDI
 protocols, all datasets have been anonymized, with no protected
 health information included."

Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

How this data was produced

The following steps were used to create the data:

• We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).
• We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.
  • We did not run any quality metrics on the scans or exclude any subjects.

Contained files

• In order to reduce the size of this dataset, for each subject, we only included the following files:

  • /mri/brain.mgz: the full brain, in FreeSurfer standard orientation ("conformed")

  • /mri/brain_mask.mgz: binary mask separating brain from background

  • /mri/aseg.mgz: brain segmentation, assigning voxels to regions. The region code for the voxel values can be found in the FreeSurferColorLUT.txt file that comes with FreeSurfer 6.

  • /mri/wm.mgz: binary mask separating white matter from everything else

All files are in FreeSurfer MGZ format.

What is NOT contained

• The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.

Author and License

Note: For the authors of the original ABIDE I dataset, see the Credits section above.

This mri volume data was created by:

Dr. Tim Schäfer
Postdoc Computational Neuroimaging
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy
University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
http://rcmd.org/ts

The data is published under the following license:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.

See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

ABIDE I FreeSurfer 6 'label' data

This archive contains the following ABIDE I FreeSurfer 6 labels: cortex, aparc, aparc.a2009s.

Credits

This data is derived from the MRI scans of the ABIDE I dataset:

• ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/

Quoting from that website:

"The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first
 ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17
 international sites, sharing previously collected resting state functional
 magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets
 made available for data sharing with the broader scientific community.
 This effort yielded 1112 dataset, including 539 from individuals with
 ASD and 573 from typical controls (ages 7-64 years, median 14.7 years
 across groups). This aggregate was released in August 2012. Its
 establishment demonstrated the feasibility of aggregating resting
 state fMRI and structural MRI data across sites; the rate of these
 data use and resulting publications (see Manuscripts) have shown its
 utility for capturing whole brain and regional properties of the brain
 connectome in Autism Spectrum Disorder (ASD). In accordance with
 HIPAA guidelines and 1000 Functional Connectomes Project / INDI
 protocols, all datasets have been anonymized, with no protected
 health information included."

Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

How this data was produced

The following steps were used to create the data:

• We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).
• We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.
  • We did not run any quality metrics on the scans or exclude any subjects.

Contained files

• In order to reduce the size of this dataset, for each subject, we only included the following files:
  • /label/lh.cortex.label : label identifying which mesh vertices belong to cortex versus medial wall, for left hemisphere.
  • /label/rh.cortex.label : label identifying which mesh vertices belong to cortex versus medial wall, for right hemisphere.
  • /label/lh.aparc.annot : Desikan atlas surface parcellation for left hemisphere.
  • /label/rh.aparc.annot : Desikan atlas surface parcellation for right hemisphere.
  • /label/lh.aparc.a2009s.annot : Destrieux atlas surface parcellation for left hemisphere.
  • /label/rh.aparc.a2900s.annot : Destrieux atlas surface parcellation for right hemisphere.

All files are in ASCII FreeSurfer label format. For the annot files, see FreeSurferColorLut.txt that comes with FreeSurfer for meaning of integers specifying a region.

What is NOT contained

• The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.

Author and License

Note: For the authors of the original ABIDE I dataset, see the Credits section above.

This label data was created by:

Dr. Tim Schäfer
Postdoc Computational Neuroimaging
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy
University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
http://rcmd.org/ts

The data is published under the following license:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.

See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.