Movielogr

movie poster

Rating: 7 stars

Tags

New York Destroy All Movies Basket Case movie night sleestakk krazykat Netflix

Seen 1 time

Seen on: 05/05/2012

Related Events

View on: IMDb | TMDb

Brain Damage (1988)

Directed by Frank Henenlotter

Horror | Comedy

Most recently watched by sensoria, krazykat, sleestakk

Overview

A normal, average guy who lives in New York City becomes dependent on an evil, disembodied brain.

Rated R | Length 86 minutes

Actors

Gordon MacDonald | Kevin Van Hentenryck | Michael Rubenstein | Beverly Bonner | John Zacherle | Ari M. Roussimoff | Rick Hearst | Jennifer Lowry | Joseph Gonzalez | Vicki Darnell | Theo Barnes | Lucille Saint-Peter | Artemis Pizarro | Bradlee Rhodes | Michael Bishop | Angel Figueroa | John Reichert | Don Henenlotter | Kenneth Packard | Slam Wedgehouse | Daniel Frye | Jeff Calder

Viewing Notes

A great, fun Henenlotter flick I’d not seen before. This is the type of movie that’s perfect for watching with a group of people.

The Basket Case cameo was awesome as was the talking brain creature, who really made the movie.

Comments

No comments yet. Log in and be the first!

  BENCHMARKS  
Loading Time: Base Classes  0.0013
Controller Execution Time ( Members / Movie Detail )  0.0224
Total Execution Time  0.0237
  GET DATA  
No GET data exists
  MEMORY USAGE  
541,888 bytes
  POST DATA  
No POST data exists
  URI STRING  
members/sensoria/movie_detail/7406
  CLASS/METHOD  
members/movie_detail
  DATABASE:  movielogr_dev (Members:$db)   QUERIES: 17 (0.0061 seconds)  (Hide)
0.0004   SELECT 1
FROM 
`ml_sessions`
WHERE `id` = 'e43360766a95014cfaef73516254751c5561172d'
AND `ip_address` = '216.73.216.111' 
0.0002   SELECT GET_LOCK('e85a7d1255521594ad4e8a5264f1d53b'300) AS ci_session_lock 
0.0003   SELECT `data`
FROM `ml_sessions`
WHERE `id` = 'e43360766a95014cfaef73516254751c5561172d'
AND `ip_address` = '216.73.216.111' 
0.0004   SELECT `username`, `user_id`
FROM `ml_users`
WHERE `username` = 'sensoria'
 
LIMIT 1 
0.0003   SELECT `MV`.`title_id`, `MT`.`tmdb_id`
FROM `ml_movie_titles` `MT`
LEFT JOIN `ml_movies` `MVON `MV`.`title_id` = `MT`.`title_id`
WHERE `MV`.`movie_id` = 7406
AND `MV`.`user_id` = 1
 LIMIT 1 
0.0004   SELECT `star_rating`
FROM `ml_users`
WHERE `ml_users`.`user_id` = 1
 LIMIT 1 
0.0002   SET SESSION group_concat_max_len 12288 
0.0018   SELECT `MV`.`title_id`, `MV`.`movie_id`, COUNT(DISTINCT MV.title_id) AS mv_count, `title`, `prefix`, `year`, `imdb_link`, `MT`.`imdb_id`, `MT`.`tmdb_id`, `overview`, `certification`, `runtime`, `genre_id_01`, `genre_id_02`, `genre_id_03`, MAX(MP.filename) AS filename, `G1`.`genre_nameAS `genre_name_01`, `G2`.`genre_nameAS `genre_name_02`, `G3`.`genre_nameAS `genre_name_03`, `date_viewed`, `notes`, `MV`.`format_id`, `FMT`.`format_name`, `MV`.`source_id`, `SRC`.`source_name`, `MV`.`device_id`, `DVC`.`device_name`, `MV`.`rating_id`, `STR`.`star_rating`, `rewatch`, GROUP_CONCAT(DISTINCT(DR.director_name) ORDER BY DR.tmdb_director_id ASC SEPARATOR "|") AS directorsGROUP_CONCAT(DISTINCT(DR.tmdb_director_id) ORDER BY DR.tmdb_director_id ASC SEPARATOR "|") AS director_idsGROUP_CONCAT(DISTINCT(ACT.actor_name) ORDER BY ACT.tmdb_actor_id ASC SEPARATOR "|") AS actorsGROUP_CONCAT(DISTINCT(ACT.tmdb_actor_id) ORDER BY ACT.tmdb_actor_id ASC SEPARATOR "|") AS actor_ids
FROM 
`ml_movie_titles` `MT`
LEFT JOIN `ml_movies` `MVON `MV`.`title_id` = `MT`.`title_id`
LEFT JOIN `ml_movie_posters` `MPON `MV`.`title_id` = `MP`.`title_id`
LEFT JOIN `ml_lookup_genres` `G1ON `MV`.`genre_id_01` = `G1`.`genre_id`
LEFT JOIN `ml_lookup_genres` `G2ON `MV`.`genre_id_02` = `G2`.`genre_id`
LEFT JOIN `ml_lookup_genres` `G3ON `MV`.`genre_id_03` = `G3`.`genre_id`
LEFT JOIN `ml_lookup_formats` `FMTON `MV`.`format_id` = `FMT`.`format_id`
LEFT JOIN `ml_lookup_sources` `SRCON `MV`.`source_id` = `SRC`.`source_id`
LEFT JOIN `ml_lookup_devices` `DVCON `MV`.`device_id` = `DVC`.`device_id`
LEFT JOIN `ml_movie_ratings_ten_star` `STRON `MV`.`rating_id` = `STR`.`rating_id`
LEFT JOIN `ml_junction_movies_directors` `JMDON `MT`.`title_id` = `JMD`.`title_id`
LEFT JOIN `ml_directors_new` `DRON `JMD`.`tmdb_director_id` = `DR`.`tmdb_director_id`
LEFT JOIN `ml_junction_movies_actors` `JMAON `MT`.`title_id` = `JMA`.`title_id`
LEFT JOIN `ml_actors_new` `ACTON `JMA`.`tmdb_actor_id` = `ACT`.`tmdb_actor_id`
WHERE `MV`.`movie_id` = 7406
AND `MV`.`user_id` = 1
ORDER BY 
`date_viewedDESC 
0.0002   SET SESSION group_concat_max_len 1024 
0.0004   SELECT `event_title`, `JME`.`event_id`
FROM `ml_junction_movies_events` `JME`
LEFT JOIN `ml_movie_events` `EVON `JME`.`event_id` = `EV`.`event_id`
WHERE `JME`.`movie_id` = 7406 
0.0002   SELECT `tag`, `JMT`.`tag_id`
FROM `ml_junction_movies_tags` `JMT`
LEFT JOIN `ml_tags` `MTON `JMT`.`tag_id` = `MT`.`tag_id`
WHERE `JMT`.`movie_id` = 7406 
0.0002   SELECT `star_rating`
FROM `ml_users`
WHERE `ml_users`.`user_id` = 1
 LIMIT 1 
0.0002   SELECT `rating_id`
FROM `ml_movies` `MV`
WHERE `MV`.`user_id` = 1
AND `MV`.`movie_id` = 7406
 LIMIT 1 
0.0001   SELECT `star_rating`
FROM `ml_movie_ratings_ten_star` `MR`
WHERE `MR`.`rating_id` = '16'
 
LIMIT 1 
0.0003   SELECT `MV2`.`date_viewed`, `MV2`.`movie_id`
FROM `ml_movies` `MV`
LEFT JOIN `ml_movies` `MV2ON `MV`.`title_id` = `MV2`.`title_idAND `MV`.`user_id` = `MV2`.`user_id`
WHERE `MV`.`user_id` = 1
AND `MV`.`movie_id` = 7406
GROUP BY 
`MV2`.`movie_id`
ORDER BY `MV2`.`date_viewedDESC 
0.0002   SELECT `comment_id`, `comment`, `commenter_id`, `timestamp`, `username`, `email_address`
FROM `ml_comments` `MC`
JOIN `ml_users` `MUON `MU`.`user_id` = `MC`.`commenter_id`
WHERE `movie_id` = 7406 
0.0003   SELECT `username`, `date_viewed`, `MV`.`movie_id`
FROM `ml_users` `MU`
JOIN `ml_movies` `MVON `MV`.`user_id` = `MU`.`user_id`
JOIN `ml_movie_titles` `MTON `MT`.`title_id` = `MV`.`title_id`
WHERE `MT`.`title_id` = 4271
ORDER BY 
`date_viewedDESC
 LIMIT 10 
  HTTP HEADERS  (Show)
  SESSION DATA  (Show)
  CONFIG VARIABLES  (Show)